DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
In response to communications sent October 23, 2023, claim(s) 1-30 and 37-40 are pending in this application; of these claims 1, 11, 16, 17, 21, and 37 are in independent form. Claims 31-36 are cancelled.
Response to Amendment
The preliminary amendments to the claims, specification, and drawings filed October 23, 2023 are acknowledged and have been entered into the record.
Drawings
The drawing(s) filed on October 23, 2023 are accepted by the Examiner.
Claim Interpretation
The claims recite the term “graph,” which the Examiner interprets as a network with nodes and edges, as in the mathematical field of graph theory. This interpretation is different from a more common use of the term graph to refer to charts and other visualizations.
Priority
The disclosure of the prior-filed application, Application No. 17384104, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The Application does not mention the graph (i.e. network) data structure (i.e. a graph that involves nodes and edges). Note that the provisional patent application 63/224,801 also lacks support or enablement. Therefore, claims 29-46 are given the filing date of another provisional patent application 63/353,481, which is June 17, 2022.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: October 23, 2023; December 6, 2023; January 31, 2024; April 19, 2024; July 19, 2024; September 13, 2024; October 30, 2024; April 10, 2025; July 23, 2025; May 4, 2026; and July 17, 2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10, 20-30, and 40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 1 and 21 recite the broad recitation “non-interface sites”, and the claim also recites “non-interface sites of the peptide backbone” using parentheses which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 1, 20, 21, and 40 recite the broad recitation “unknown sites”, and the claim also recites “unknown non-interface sites” using parentheses which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claims 2-10 and 22-30 are rejected because they depend on rejected base claims and include the limitations of the rejected claims.
The Examiner suggests not using parentheses, which creates ambiguity about the intended scope of the claims.
Claim Analysis
No rejection is made regarding subject-matter eligibility because of the use of a particular specialized data structure that is integrated with the abstract steps and/or configuration.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-30 and 37-40 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2024/0412810 A1 (“Ingraham”). Note that the filing date of the Ingraham reference is based on the provisional patent application 63/261,646 filed September 24, 2021.
As to claim 1, Ingraham teaches a method for the in-silico design of an amino acid sequence of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, a scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biological complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”) oriented at particular pose relative to the target (Ingraham para [0071]: edge features include distance and orientation between monomers),
wherein the peptide backbone comprises a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), and
wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains), and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences);
(b) generating, by the processor, using a machine learning model, (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the sequence prediction for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or (the broadest reasonable interpretation of “and/or” is “or”) using the predicted sequence to design the amino acid sequence of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 2, Ingraham teaches the method of claim 1, wherein the sequence prediction comprises an identification of a particular amino acid side chain type for each of at least a portion of the unknown non-interface sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 3, Ingraham teaches the method of claim 1, wherein all of the interface sites are unknown sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 4, Ingraham teaches the method of claim 1, wherein a subset of the interface sites are known sites (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 5, Ingraham teaches the method of claim 1, wherein the target is a protein and/or peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) having a known sequence, such that a majority of target amino acid sites are known sites, having a known amino acid side chain type (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 6, Ingraham teaches the method of claim 1, wherein the target is a protein and/or peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) having a known backbone conformation, but an unknown sequence, such that a majority of target amino acid sites are unknown sites, having an unknown and/or to-be determined amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 7, Ingraham teaches the method of claim 1, wherein the scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprises a plurality of target nodes, each corresponding to and representing a particular target amino acid site (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph and may represent interface sites, i.e. involving targets).
As to claim 8, Ingraham teaches the method of claim 7, wherein each target node comprises an amino acid encoding component comprising, for each known target node, values representing a particular type of amino acid side chain, and, for each unknown target node, one or more masking values (Ingraham Para [0006]: “a hybrid approach of using known/experimentally determined backbone structures and modeled backbone structures (e.g., in silico generated backbone structures), such as designing part of a backbone structure of a biopolymer sequence, but leaving a portion of the experimentally derived portion intact”).
As to claim 9, Ingraham teaches the method of claim 1, wherein the scaffold target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprises a plurality of scaffold nodes, each corresponding to and representing a particular amino acid site of the peptide backbone (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph) of the custom biologic (Ingraham Para [0067]: a backbone polymer structure represented by the graph).
As to claim 10, Ingraham teaches the method of claim 9, wherein each scaffold node comprises an amino acid encoding component comprising, for each known scaffold node, values representing a particular type of amino acid side chain, and, for each unknown scaffold node, one or more masking values (Ingraham Para [0006]: “a hybrid approach of using known/experimentally determined backbone structures and modeled backbone structures (e.g., in silico generated backbone structures), such as designing part of a backbone structure of a biopolymer sequence, but leaving a portion of the experimentally derived portion intact”).
As to claim 11, Ingraham teaches a method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence; Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, a graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) representation of the polypeptide complex comprising a plurality polypeptide chains (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges of the polypeptide), each having a particular peptide backbone structure (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”) and oriented at a particular pose relative to other members of the complex (Ingraham para [0071]: edge features include distance and orientation between monomers), wherein each polypeptide chain comprises a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains);
(b) generating, by the processor, using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the one or more sequence predictions for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or (the broadest reasonable interpretation of “and/or” is “or”) using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 12, Ingraham teaches the method of claim 11, wherein:
for at least one particular member chain, a subset of the amino acid sites of the particular member chain are interface sides (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), each interface site located in proximity to one or more amino acid sites on other members of the polypeptide complex (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), and wherein (i) each interface site is an unknown site (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains) and (ii) a majority of remaining non-interface sites of the particular member chain are unknown sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences), and
step (b) comprises generating a sequence prediction (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network) for the particular member chain that comprises an identification of an amino acid side chain type for each unknown interface site of the particular member chain (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure).
As to claim 13, Ingraham teaches the method of claim 12, where the sequence prediction for the particular member chain further comprises an identification of an amino acid side chain type for each of at least a portion of the unknown non-interface sites of the particular member chain (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 14, Ingraham teaches the method of claim 11, wherein all of the polypeptide chains have a same peptide backbone (Ingraham Para [0064]: generated polymers may be fully redesigned sequences.
As to claim 15, Ingraham teaches the method of claim 11, wherein two or more of the polypeptide chains have a different peptide backbone (Ingraham Para [0006]: “a hybrid approach of using known/experimentally determined backbone structures and modeled backbone structures (e.g., in silico generated backbone structures), such as designing part of a backbone structure of a biopolymer sequence, but leaving a portion of the experimentally derived portion intact”).
As to claim 16, Ingraham teaches a method for the in-silico prediction of a protein sequence of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence; Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, a graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) representation of a peptide backbone of the protein (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges of a peptide; Ingraham Para [0005]: the reference structure includes a target complex), the peptide backbone comprising a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains);
(b) generating, by the processor, using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the sequence prediction for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or (the broadest reasonable interpretation of “and/or” is “or”) and/or using the sequence predictions to design amino acid sequences of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 17, Ingraham teaches a method for the in-silico design of an amino acid sequence of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, a scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biological complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”) oriented at particular pose relative to the target (Ingraham para [0071]: edge features include distance and orientation between monomers),
wherein the peptide backbone comprises a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains);
(b) generating, by the processor, using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the sequence prediction for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or using the predicted sequence to design the amino acid sequence of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 18, Ingraham teaches the method of claim 17, wherein at least a portion of the unknown sites are unknown interface sites and wherein the sequence prediction comprises, for each of at least a portion of the unknown interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 19, Ingraham teaches the method of claim 17, wherein at least a portion of the unknown sites are unknown non-interface sites (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains) and wherein the sequence prediction comprises, for each of at least a portion of the unknown non-interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 20, Ingraham teaches the method of claim 19, wherein substantially all non-interface sites of the custom biologic are unknown (non-interface) sites (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains).
As to claim 21, Ingraham teaches a system for the in-silico design of an amino acid sequence of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the system comprising:
a processor of a computing device; and
memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to
(a) receive a scaffold-target complex graph comprising a graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) representation of at least a portion of a biological complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”) oriented at particular pose relative to the target (Ingraham para [0071]: edge features include distance and orientation between monomers),
wherein the peptide backbone comprises a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), and
wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains), and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences);
(b) generate, using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) provide the sequence prediction for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or (the broadest reasonable interpretation of “and/or” is “or”) and/or using the predicted sequence to design the amino acid sequence of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 22, Ingraham teaches the system of claim 21, wherein the sequence prediction comprises an identification of a particular amino acid side chain type for each of at least a portion of the unknown non-interface sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 23, Ingraham teaches the system of claim 21, wherein all of the interface sites are unknown sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 24, Ingraham teaches the system of claim 21, wherein a subset of the interface sites are known sites (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 25, Ingraham teaches the system of claim 21, wherein the target is a protein and/or peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) having a known sequence, such that a majority of target amino acid sites are known sites, having a known amino acid side chain type (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 26, Ingraham teaches the system of claim 21, wherein the target is a protein and/or peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) having a known backbone conformation, but an unknown sequence, such that a majority of target amino acid sites are unknown sites, having an unknown and/or to-be determined amino acid side chain type (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
As to claim 27, Ingraham teaches the system of claim 21, wherein the scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprises a plurality of target nodes, each corresponding to and representing a particular target amino acid site (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph and may represent interface sites, i.e. involving targets).
As to claim 28, Ingraham teaches the system of claim 27, wherein each target node comprises an amino acid encoding component comprising, for each known target node, values representing a particular type of amino acid side chain, and, for each unknown target node, one or more masking values (Ingraham Para [0006]: “a hybrid approach of using known/experimentally determined backbone structures and modeled backbone structures (e.g., in silico generated backbone structures), such as designing part of a backbone structure of a biopolymer sequence, but leaving a portion of the experimentally derived portion intact”).
As to claim 29, Ingraham teaches the system of claim 21, wherein the scaffold target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprises a plurality of scaffold nodes, each corresponding to and representing a particular amino acid site of the peptide backbone (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph) of the custom biologic (Ingraham Para [0067]: a backbone polymer structure represented by the graph).
As to claim 30, Ingraham teaches the system of claim 29, wherein each scaffold node comprises an amino acid encoding component comprising, for each known scaffold node, values representing a particular type of amino acid side chain, and, for each unknown scaffold node, one or more masking values (Ingraham Para [0006]: “a hybrid approach of using known/experimentally determined backbone structures and modeled backbone structures (e.g., in silico generated backbone structures), such as designing part of a backbone structure of a biopolymer sequence, but leaving a portion of the experimentally derived portion intact”).
As to claim 37, Ingraham teaches a system for the in-silico design of an amino acid sequence of a custom biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the system comprising:
a processor of a computing device; and
memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to:
(a) receive a scaffold-target complex graph comprising a graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) representation of at least a portion of a biological complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”) oriented at particular pose relative to the target (Ingraham para [0071]: edge features include distance and orientation between monomers),
wherein the peptide backbone comprises a plurality of amino acid sites (Ingraham Para [0066]: peptide backbone of a biopolymer complex), substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains);
(b) generate, using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) provide the sequence prediction for use in designing the custom biologic (Ingraham Para [0064]: generating new interfaces) and/or (the broadest reasonable interpretation of “and/or” is “or”) and/or (the broadest reasonable interpretation of “and/or” is “or”) using the predicted sequence to design the amino acid sequence of the custom biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 38, Ingraham teaches the system of claim 37, wherein at least a portion of the unknown sites are unknown interface sites (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains) and wherein the sequence prediction comprises, for each of at least a portion of the unknown interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure).
As to claim 39, Ingraham teaches the system of claim 37, wherein at least a portion of the unknown sites are unknown non-interface sites (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains) and wherein the sequence prediction comprises, for each of at least a portion of the unknown non-interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure).
As to claim 40, Ingraham teaches the system of claim 39, wherein substantially all non-interface sites of the custom biologic are unknown (non-interface) sites (Ingraham Para [0064]: generated polymers may be fully redesigned sequences).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 11, 16, 17, 21, and 37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 27, and 28 of U.S. Patent No. 11,450,407. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a genus of the patented species with minor variations that are at once envisaged.
Instant Application 18/216,172
Reference Patent US 11,450,407
1. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target in-silico via a pipeline of artificial intelligence (AI)-powered modules, the method comprising: (a) receiving and/or generating, by a processor of a computing device, one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising a candidate peptide backbone at a particular pose with respect to the target, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and is represented using a scaffold model that identifies types and locations of peptide backbone atoms while omitting amino-acid side chain atoms; (b) for each of the one or more prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a first machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model; (c) selecting, by the processor, a scaffold-target complex model of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores, thereby identifying a selected candidate peptide backbone, oriented at a selected pose, on which to build a custom interface portion of a ligand for binding to the target; (d) generating, by the processor, based on the selected scaffold-target complex model, one or more prospective ligand-target complex models, each representing a prospective ligand corresponding to the selected candidate peptide backbone (i) comprising at least an interface region located in proximity to the target populated with amino acids and (ii) positioned with respect to the target based on the selected pose, each prospective ligand comprising a particular amino acid population at its interface region; (e) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a second machine learning model, thereby determining one or more interface scores; (f) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (g) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
11. A method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of the polypeptide complex comprising a plurality polypeptide chains, each having a particular peptide backbone structure and oriented at a particular pose relative to other members of the complex, wherein each polypeptide chain comprises a plurality of amino acid sites, substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions; and (c) providing the one or more sequence predictions for use in designing the custom biologic and/or using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target in-silico via a pipeline of artificial intelligence (AI)-powered modules, the method comprising: (a) receiving and/or generating, by a processor of a computing device, one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising a candidate peptide backbone at a particular pose with respect to the target, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and is represented using a scaffold model that identifies types and locations of peptide backbone atoms while omitting amino-acid side chain atoms; (b) for each of the one or more prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a first machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model; (c) selecting, by the processor, a scaffold-target complex model of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores, thereby identifying a selected candidate peptide backbone, oriented at a selected pose, on which to build a custom interface portion of a ligand for binding to the target; (d) generating, by the processor, based on the selected scaffold-target complex model, one or more prospective ligand-target complex models, each representing a prospective ligand corresponding to the selected candidate peptide backbone (i) comprising at least an interface region located in proximity to the target populated with amino acids and (ii) positioned with respect to the target based on the selected pose, each prospective ligand comprising a particular amino acid population at its interface region; (e) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a second machine learning model, thereby determining one or more interface scores; (f) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (g) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
16. A method for the in-silico prediction of a protein sequence of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of a peptide backbone of the protein, the peptide backbone comprising a plurality of amino acid sites, a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain; (b) generating, by the processor, using a machine learning model, a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the sequence predictions to design amino acid sequences of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target in-silico via a pipeline of artificial intelligence (AI)-powered modules, the method comprising: (a) receiving and/or generating, by a processor of a computing device, one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising a candidate peptide backbone at a particular pose with respect to the target, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and is represented using a scaffold model that identifies types and locations of peptide backbone atoms while omitting amino-acid side chain atoms; (b) for each of the one or more prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a first machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model; (c) selecting, by the processor, a scaffold-target complex model of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores, thereby identifying a selected candidate peptide backbone, oriented at a selected pose, on which to build a custom interface portion of a ligand for binding to the target; (d) generating, by the processor, based on the selected scaffold-target complex model, one or more prospective ligand-target complex models, each representing a prospective ligand corresponding to the selected candidate peptide backbone (i) comprising at least an interface region located in proximity to the target populated with amino acids and (ii) positioned with respect to the target based on the selected pose, each prospective ligand comprising a particular amino acid population at its interface region; (e) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a second machine learning model, thereby determining one or more interface scores; (f) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (g) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
17. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target in-silico via a pipeline of artificial intelligence (AI)-powered modules, the method comprising: (a) receiving and/or generating, by a processor of a computing device, one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising a candidate peptide backbone at a particular pose with respect to the target, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and is represented using a scaffold model that identifies types and locations of peptide backbone atoms while omitting amino-acid side chain atoms; (b) for each of the one or more prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a first machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model; (c) selecting, by the processor, a scaffold-target complex model of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores, thereby identifying a selected candidate peptide backbone, oriented at a selected pose, on which to build a custom interface portion of a ligand for binding to the target; (d) generating, by the processor, based on the selected scaffold-target complex model, one or more prospective ligand-target complex models, each representing a prospective ligand corresponding to the selected candidate peptide backbone (i) comprising at least an interface region located in proximity to the target populated with amino acids and (ii) positioned with respect to the target based on the selected pose, each prospective ligand comprising a particular amino acid population at its interface region; (e) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a second machine learning model, thereby determining one or more interface scores; (f) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (g) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
21. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
27. A system for designing a custom biologic structure for binding to a target in-silico via a pipeline of artificial intelligence (AI)-powered modules, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive and/or generate one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising a candidate peptide backbone at a particular pose with respect to the target, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and is represented using a scaffold model that identifies types and locations of peptide backbone items while omitting amino-acid side chain atoms; (b) for each of the one or more prospective scaffold-target complex models, determine a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a first machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model; (c) select a scaffold-target complex model of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores, thereby identifying a selected candidate peptide backbone, oriented at a selected pose, on which to build a custom interface portion of a ligand for binding to the target; (d) generate, based on the selected scaffold-target complex model, one or more prospective ligand-target complex models, each representing a prospective ligand corresponding to the selected candidate peptide backbone (i) comprising at least an interface region located in proximity to the target molecule populated with amino acids and (ii) positioned with respect to the target based on the selected pose, each prospective ligand comprising a particular amino acid population at its interface region; (e) for each of the one or more prospective ligand-target complex models, determine an interface score using a second machine learning model, thereby determining one or more interface scores; (f) select a subset of the prospective ligand-target complex models based on the one or more interface scores; and (g) provide the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
37. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to: (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
28. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising: (a) receiving and/or generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model represents the candidate peptide backbone by identifying types and locations of peptide backbone atoms while omitting amino-acid side chain atoms; (b) generating, by the processor, for the candidate scaffold model, one or more prospective scaffold-target complex models, each representing at least a portion of a complex comprising the candidate peptide backbone at a particular pose with respect to the target; (c) for each of the one or more prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, thereby determining one or more scaffold pose scores, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding 3D volumetric representation; and using the corresponding 3D volumetric representation as input to a machine learning model that determines, as output, the scaffold pose score, wherein the scaffold pose score is a value representing a measure of plausibility that the particular prospective scaffold-target complex model represents a native complex, thereby determining one or more scaffold pose scores; (d) selecting, by the processor, a subset of the one or more prospective scaffold-target complex models using the determined one or more scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
Claims 1, 11, 16, 17, 21, and 37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 56 and 58 of U.S. Patent No. 12,738,342. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a genus of the patented species with minor variations that are at once envisaged.
Instant Application 18/216,172
Reference patent US 12,738,342
1. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
11. A method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of the polypeptide complex comprising a plurality polypeptide chains, each having a particular peptide backbone structure and oriented at a particular pose relative to other members of the complex, wherein each polypeptide chain comprises a plurality of amino acid sites, substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions; and (c) providing the one or more sequence predictions for use in designing the custom biologic and/or using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
16. A method for the in-silico prediction of a protein sequence of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of a peptide backbone of the protein, the peptide backbone comprising a plurality of amino acid sites, a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain; (b) generating, by the processor, using a machine learning model, a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the sequence predictions to design amino acid sequences of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
17. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
21. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
37. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to: (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
56. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered scaffold docker module, the method comprising:(a) generating, by a processor of a computing device, a candidate scaffold model, wherein the candidate scaffold model is a representation of at least a portion of a candidate peptide backbone, wherein the candidate peptide backbone is a prospective backbone of the custom biologic structure being designed and wherein the candidate scaffold model (i) comprises representations of peptide backbone atoms identifying types and locations of peptide backbone atoms, but (ii excluding side chains;(b) generating, by the processor, for the candidate scaffold model, a plurality of prospective scaffold-target complex models, each representing at least a portion of a complex comprising the target and the candidate peptide backbone at a particular pose with respect to the target;(c) for each of the plurality of prospective scaffold-target complex models, determining, by the processor, a scaffold pose score, wherein determining the scaffold pose score for each particular one of the one or more prospective scaffold-target complex models comprises: generating, based on the particular scaffold-target complex model, a corresponding representation; and using the corresponding representation as input to a machine learning model that determines, as output, the scaffold pose score for the particular scaffold-target complex model, said machine learning model having been trained using representations of native scaffold-target complex models, each representing a peptide backbone of a particular protein and/or peptide, as oriented at a particular pose with respect to a target, in an existing native protein-protein and/or protein-peptide example complex, and wherein the scaffold pose score is a likelihood that the particular scaffold-target complex model represents a native complex; (d) selecting, by the processor, a subset of the plurality of prospective scaffold-target complex models using the determined scaffold pose scores; and (e) providing the selected subset of prospective scaffold-target complex models for use in designing the custom biologic structure for binding to the target.
Claims 1, 11, 16, 17, 21, and 37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 16 of U.S. Patent No. 11,869,629. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a genus of the patented species with minor variations that are at once envisaged.
Instant Application 18/216,172
Reference Patent US 11,869,629
1. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the method comprising: (a) receiving and/or generating, by a processor of a computing device, a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generating, by the processor, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a machine learning model, thereby determining one or more interface scores; (d) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
11. A method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of the polypeptide complex comprising a plurality polypeptide chains, each having a particular peptide backbone structure and oriented at a particular pose relative to other members of the complex, wherein each polypeptide chain comprises a plurality of amino acid sites, substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions; and (c) providing the one or more sequence predictions for use in designing the custom biologic and/or using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the method comprising: (a) receiving and/or generating, by a processor of a computing device, a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generating, by the processor, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a machine learning model, thereby determining one or more interface scores; (d) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
16. A method for the in-silico prediction of a protein sequence of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of a peptide backbone of the protein, the peptide backbone comprising a plurality of amino acid sites, a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain; (b) generating, by the processor, using a machine learning model, a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the sequence predictions to design amino acid sequences of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the method comprising: (a) receiving and/or generating, by a processor of a computing device, a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generating, by the processor, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a machine learning model, thereby determining one or more interface scores; (d) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
17. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for designing a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the method comprising: (a) receiving and/or generating, by a processor of a computing device, a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generating, by the processor, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determining, by the processor, an interface score using a machine learning model, thereby determining one or more interface scores; (d) selecting, by the processor, a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) providing the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
21. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
16. A system for in silico design of a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive and/or generate a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generate, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determine an interface score using a machine learning model, thereby determining one or more interface scores; (d) select a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) provide the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
37. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to: (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
16. A system for in silico design of a custom biologic structure for binding to a target via an artificial intelligence (AI)-powered interface designer module, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive and/or generate a scaffold-target complex model that represents at least a portion of a complex comprising the target and a particular candidate peptide backbone on which to build a custom interface portion of a ligand for binding to the target, wherein the particular candidate peptide backbone is oriented at a particular pose with respect to the target and the scaffold-target complex model comprises a representation of (i) at least a portion of the target and (ii) at least a portion of the candidate peptide backbone, said portions of the target and candidate peptide backbone located in proximity to each other and forming a potential binding interface; (b) generate, based on the scaffold-target complex model, one or more prospective ligand-target complex models, each representing at least a portion of a complex comprising the target and a particular prospective ligand, each particular prospective ligand: (i) having a peptide backbone corresponding to the particular candidate peptide backbone, (ii) positioned with respect to the target based on the particular pose, and (iii) comprising at least an interface region, located in proximity to the target, populated with amino acids, such that each particular prospective ligand comprises a particular amino acid population at its interface region; (c) for each of the one or more prospective ligand-target complex models, determine an interface score using a machine learning model, thereby determining one or more interface scores; (d) select a subset of the prospective ligand-target complex models based on at least a portion of the one or more interface scores; and (e) provide the selected subset of prospective ligand-target complex models for use in designing the custom biologic structure for binding to the target.
Claims 1, 11, 16, 17, 21, and 37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 17 of U.S. Patent No. 11,742,057. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a genus of the patented species with minor variations that are at once envisaged.
Instant Application 18/216,172
Reference Patent US 11,742,057
1. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for the in-silico design of an amino acid interface of a biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generating, by the processor, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with one or more likelihood values; (c) using, by the processor, the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and, (d) providing the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
11. A method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of the polypeptide complex comprising a plurality polypeptide chains, each having a particular peptide backbone structure and oriented at a particular pose relative to other members of the complex, wherein each polypeptide chain comprises a plurality of amino acid sites, substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions; and (c) providing the one or more sequence predictions for use in designing the custom biologic and/or using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic.
1. A method for the in-silico design of an amino acid interface of a biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generating, by the processor, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with one or more likelihood values; (c) using, by the processor, the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and, (d) providing the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
16. A method for the in-silico prediction of a protein sequence of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of a peptide backbone of the protein, the peptide backbone comprising a plurality of amino acid sites, a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain; (b) generating, by the processor, using a machine learning model, a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the sequence predictions to design amino acid sequences of the custom biologic.
1. A method for the in-silico design of an amino acid interface of a biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generating, by the processor, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with one or more likelihood values; (c) using, by the processor, the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and, (d) providing the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
17. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
1. A method for the in-silico design of an amino acid interface of a biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generating, by the processor, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with one or more likelihood values; (c) using, by the processor, the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and, (d) providing the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
21. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
17. A system for the in-silico design of an amino acid interface of a biologic for binding to a target, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generate, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with one or more likelihood values; (c) use the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and (d) provide the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
37. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to: (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
17. A system for the in-silico design of an amino acid interface of a biologic for binding to a target, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target and a peptide backbone of the in-progress custom biologic, the initial scaffold-target complex graph comprising: a target graph representing at least a portion of the target; and a scaffold graph representing at least a portion of the peptide backbone of the in-progress custom biologic, the scaffold graph comprising a plurality of scaffold nodes, a subset of which are unknown interface nodes, wherein each of said unknown interface nodes: (i) represents a particular amino acid interface site, along the peptide backbone of the in-progress custom biologic, that is located in proximity to one or more amino acids of the target, and (ii) has a corresponding node feature vector comprising a side chain type component vector populated with one or more masking values, thereby representing an unknown, to-be determined, amino acid side chain; (b) generate, using a machine learning model, one or more likelihood graphs based on the initial scaffold-target complex graph, each of the one or more likelihood graphs comprising a plurality of nodes, a subset of which are classified interface nodes, each of which: (i) corresponds to a particular unknown interface node of the scaffold graph and represents a same particular interface site along the peptide backbone of the in-progress custom biologic as the corresponding particular interface node, and (ii) has a corresponding node feature vector comprising a side chain component vector populated with on or more likelihood values; (c) use the one or more likelihood graphs to determine a predicted interface comprising, for each interface site, an identification of a particular amino acid side chain type; and (d) provide the predicted interface for use in designing the amino acid interface of the in-progress custom biologic and/or using the predicted interface to design the amino acid interface of the in-progress custom biologic.
Claims 1, 11, 16, 17, 21, and 37 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 29, 30, and 50 of copending Application No. 18/219,325 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a genus of the claimed species of the reference application with minor variations that are at once envisaged.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application 18/216,172
Reference application 18/219,325
1. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
29. A method for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the method comprising:(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the in-progress custom biologic;(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and(c) providing the predicted interface for use in designing the amino acid interface of the biologic and/or using the predicted interface to design the amino acid interface of the biologic.
11. A method for the in-silico prediction sequences of one or more chains of a polypeptide complex of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of the polypeptide complex comprising a plurality polypeptide chains, each having a particular peptide backbone structure and oriented at a particular pose relative to other members of the complex, wherein each polypeptide chain comprises a plurality of amino acid sites, substantially all of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, for each particular chain of at least a portion of the plurality of polypeptide chains, a sequence prediction comprising, for each of at least a portion of the unknown sites of the particular chain, an identification of a particular amino acid side chain type, thereby generating one or more sequence predictions; and (c) providing the one or more sequence predictions for use in designing the custom biologic and/or using the one or more sequence predictions to design amino acid sequences of the polypeptide complex of the custom biologic.
29. A method for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the method comprising:(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the in-progress custom biologic;(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and(c) providing the predicted interface for use in designing the amino acid interface of the biologic and/or using the predicted interface to design the amino acid interface of the biologic.
16. A method for the in-silico prediction of a protein sequence of a custom biologic, the method comprising: (a) receiving, by a processor of a computing device, a graph representation of a peptide backbone of the protein, the peptide backbone comprising a plurality of amino acid sites, a majority of which are unknown sites, having an unknown and/or to-be-determined amino acid side chain; (b) generating, by the processor, using a machine learning model, a sequence prediction for the protein comprising, for at least a portion of the unknown sites, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the sequence predictions to design amino acid sequences of the custom biologic.
29. A method for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the method comprising:(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the in-progress custom biologic;(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and(c) providing the predicted interface for use in designing the amino acid interface of the biologic and/or using the predicted interface to design the amino acid interface of the biologic.
17. A method for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the method comprising: (a) receiving, by a processor of a computing device, a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generating, by the processor, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) providing the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
50. A method for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the method comprising:(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the in-progress custom biologic;(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a plurality of transformer layers, a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and(c) providing the predicted interface for use in designing the amino acid interface of the biologic and/or using the predicted interface to design the amino acid interface of the biologic.
21. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, a subset of which are interface sites, each interface site located in proximity to one or more amino acid sites of the target, and wherein (i) each of at least a portion the interface sites is an unknown interface site, having an unknown and/or to-be-determined amino acid side chain type, and (ii) substantially all of remaining, non-interface, sites (of the peptide backbone) are unknown (non-interface) sites, having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising, for each unknown interface site of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
30. A system for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the biologic; (b) generate, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and (c) provide the predicted interface for use in designing the amino acid interface of the biologic and/or use the predicted interface to design the amino acid interface of the biologic.
37. A system for the in-silico design of an amino acid sequence of a custom biologic for binding to a target, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed, cause the processor to: (a) receive a scaffold-target complex graph comprising a graph representation of at least a portion of a biological complex comprising the target and a peptide backbone of the custom biologic oriented at particular pose relative to the target, wherein the peptide backbone comprises a plurality of amino acid sites, substantially all of which are unknown sites having an unknown and/or to-be-determined amino acid side chain type; (b) generate, using a machine learning model, a sequence prediction for the custom biologic, the sequence prediction comprising for each of at least a portion of the unknown sites of the peptide backbone, an identification of a particular amino acid side chain type; and (c) provide the sequence prediction for use in designing the custom biologic and/or using the predicted sequence to design the amino acid sequence of the custom biologic.
30. A system for the in-silico design of an amino acid interface of a biologic for binding to a target protein, the system comprising: a processor of a computing device; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive an initial scaffold-target complex graph comprising a graph representation of at least a portion of a biologic complex comprising the target protein and a peptide backbone of the biologic; (b) generate, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type; and (c) provide the predicted interface for use in designing the amino acid interface of the biologic and/or use the predicted interface to design the amino acid interface of the biologic.
Conclusion
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 September 11, 2026
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