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 30, 2023 claim(s) 29-46 are pending in this application; of these claims 29 and 30 are in independent form. Claims 1-28 are cancelled.
Response to Amendment
The preliminary amendments filed October 30, 2023 are acknowledged and have been entered into the record. This includes the amendments to the claims, the specification, and the drawings.
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 provisional patent application 63/353,481, which is June 17, 2022.
Drawings
The drawings are objected to because Figure 5B mentions the colors green and blue despite that it is a black-and-white drawing. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because it is more than 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
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: December 1, 2023; January 31, 2024; March 1, 2024; April 19, 2024; July 19, 2024; September 13, 2024; October 30, 2024; April 10, 2025; and July 7, 2025.
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 29-46 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.
Claims 31-37 and 39-45 are rejected because they depend from a rejected base claim.
Claim Rejections - 35 USC § 101
The claims recite a mental process of using a predicted interface to design the amino acid interface of biologic. Paragraph [0020] of Applicant’s Specification suggests that this is may be an in silico design. Hence, the Examiner interprets the design step as a mental process.
Regarding dependent claims 38 and 46, the steps of receiving a graph representation and applying a machine learning model to the graph representation is a specialized computer with a specialized data structure and a specialized machine learning algorithm applicable to graph data structures. Therefore, the additional elements are not merely instructions to apply instructions on a general purpose computer or a general purpose machine learning platform. Furthermore, Applicant’s Figures 6 and 7 provide statistical evidence that the output of the graph-based neural network accomplishes an design optimization that is difficult to perform as a mental process without the computational aid of the graph-based neural network. The Examiner is treating the graph-based neural network as an additional element that not routine and conventional in the context of drug design. However, this finding does not necessarily mean that the claimed invention is non-obvious over the prior art.
However, regarding claims 29-37 and 39-45, the claims do not recite any limitations that indicate that the machine learning algorithm is applicable to graph data structures. The independent claims recite receiving a graph representation but then recite “generating, using a machine learning model, a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type”. This step does not indicate that the received graph representation is utilized as input for this machine learning model and therefore is not limited to being applicable to graph data structures.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 29-37 and 39-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process of using a predicted interface to design the amino acid interface of biologic. This judicial exception is not integrated into a practical application because the additional elements are a general purpose computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply the mental process on a judicial exception on a general purpose computer is not an inventive concept. See Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980. The dependent claims are merely limitations to the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 29-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220372068 A1 (“Kim”) in view of US 20200342953 A1 (“Morrone”).
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
As to claim 29, Kim teaches a method for the in-silico design of an amino acid (Kim Para [0113]: a computerized design of an amino acid sequence) interface of a biologic for binding to a target (Kim Para [0128]: for generating a sequence of serum albumin, which is known to one of ordinary skill in the art to have various binding properties with various nature ligands), the method comprising:
(a) receiving, by a processor of a computing device, an initial [[ (Kim Para [0114]-[0114]: receiving a partially filed in protein sequence and an edge value set);
(b) generating, by the processor, using a machine learning model (Kim Para [0118]: generating a graph convolution), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Kim Para [0118]: the graph convolution predicting a plurality of amino acid values); and
(c) providing the predicted interface for use in designing the amino acid interface of biologic (Kim Para [0187]: the predicted amino acid interface are determined for serum albumin) and/or (the broadest reasonable interpretation of “and/or” is disjunction) using the predicted interface to design the amino acid interface of biologic (Kim Para [0187]: the predicted amino acid interface for serum albumin are constructed) .
However, Kim does not teach that the graph comprises a representation of a 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 biologic.
Nevertheless, Morrone teaches a graph comprising a representation of a 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 biologic (Morrone Figure 2 and Para [0030]-[0031]: construction of structural graphs having an interface between target and peptide in a ligand-protein graph generator).
The Examiner argues that this is obvious because it is a instance of known work in one field prompting variations of it in a different one based on design incentives or other market forces when the variations are predictable to one of ordinary skill in the art (MPEP § 2143(I)(F)). This is because Kim teaches a similar device for determining the sequence of a protein having a particular backbone structure. There are design incentives for drug discovery to prompt adaptation of Kim’s invention to not only construct a particular protein (e.g., to know its solubility and shape), but also to know whether it can feasibly bind a target for drug discovery. The differences between the claimed invention and the prior art are encompassed by a variation taught in the prior art in Morrone, as noted above. One of ordinary skill in the art would have been able to replace the single protein in Kim with the protein-ligand of Morrone. There would be a reasonable expectation of success to one of ordinary skill in the art, before the effective filing date of the claimed invention, because each invention relies on graph representations of the proteins/peptides for efficient computation. They are each in the field of computational chemistry using similar methods to address related problems.
As to claim 30, Kim teaches a system for the in-silico design of an amino acid (Kim Para [0113]: a computerized design of an amino acid sequence) interface of a biologic for binding to a target (Kim Para [0128]: for generating a sequence of serum albumin, which is known to one of ordinary skill in the art to have various binding properties with various nature ligands), the system comprising:
a processor of a computing device (Kim Para [0054]: a computer-implemented system); and
a memory having instructions stored thereon, wherein the instructions, when executed by the processor (Kim Para [0054]: a computer-implemented system), cause the processor to:
(a) receive an initial [[ (Kim Para [0114]-[0115]: receiving a partially filed in protein sequence and an edge value set);
(b) generate, using a machine learning model (Kim Para [0118]: generating a graph convolution), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Kim Para [0118]: the graph convolution predicting a plurality of amino acid values); and
(c) provide the predicted interface for use in designing the amino acid interface of biologic (Kim Para [0187]: the predicted amino acid interface are determined for serum albumin) and/or (the broadest reasonable interpretation of “and/or” is disjunction) use the predicted interface to design the amino acid interface of the biologic (Kim Para [0187]: the predicted amino acid interface for serum albumin are constructed).
However, Kim does not teach that the graph comprises a representation of a 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 biologic.
Nevertheless, Morrone teaches a graph comprising a representation of a 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 biologic (Morrone Figure 2 and Para [0030]-[0031]: construction of structural graphs having an interface between target and peptide in a ligand-protein graph generator).
The Examiner argues that this is obvious because it is a instance of known work in one field prompting variations of it in a different one based on design incentives or other market forces when the variations are predictable to one of ordinary skill in the art (MPEP § 2143(I)(F)). This is because Kim teaches a similar device for determining the sequence of a protein having a particular backbone structure. There are design incentives for drug discovery to prompt adaptation of Kim’s invention to not only construct a particular protein (e.g., to know its solubility and shape), but also to know whether it can feasibly bind a target for drug discovery. The differences between the claimed invention and the prior art are encompassed by a variation taught in the prior art in Morrone, as noted above. One of ordinary skill in the art would have been able to replace the single protein in Kim with the protein-ligand of Morrone. There would be a reasonable expectation of success to one of ordinary skill in the art, before the effective filing date of the claimed invention, because each invention relies on graph representations of the proteins/peptides for efficient computation. They are each in the field of computational chemistry using similar methods to address related problems.
As to claim 31, Kim in view of Morrone teaches the method of claim 29, wherein the initial scaffold-target complex graph comprises a plurality of nodes and edges (Kim Para [0114]-[0114]: receiving a partially filed in protein sequence as nodes and an edge value set).
As to claim 32, Kim in view of Morrone teaches the method of claim 29, wherein the initial scaffold-target complex graph comprises a scaffold graph representing at least a portion of the peptide backbone of the biologic (Kim Para [0114]-[0114]: receiving a partially filed in protein sequence), the scaffold graph comprising a plurality of scaffold nodes, each representing a particular amino acid site of the peptide backbone (Kim Para [0056]: the nodes of the graph represent amino acids and missing values along the sequence of the protein).
As to claim 33, Kim in view of Morrone teaches the method of claim 32, wherein a subset of the scaffold nodes are unknown interface nodes, each representing a particular amino acid interface site located in proximity to the target and having an unknown, to-be-determined amino acid side chain (Kim Para [0056]: some of the amino acid positions have undefined missing values).
As to claim 34, Kim in view of Morrone teaches the method of claim 32, wherein a subset of the scaffold nodes are known scaffold nodes, each representing a particular amino acid site having a known side chain type (Kim Para [0056]: some of the amino acid positions are filled in).
As to claim 35, Kim in view of Morrone teaches the method of claim 32, wherein the scaffold graph comprises a plurality of scaffold edges, each associated with two particular scaffold nodes and representing a relative position (Kim Para [0123]: the edges of the graph is determined by an edge index that specifies amino acids that have physical interaction) and/or (the broadest reasonable interpretation of “and/or” is disjunction) orientation of two amino acid sites represented by the two particular scaffold nodes (Kim Para [0072]: other interactions, such as specifying the orientation of residues with respect to one another).
As to claim 36, Kim in view of Morrone teaches method of claim 29, wherein the target is or comprises a protein (Morrone Para [0020]: “potential drug molecules”; the Examiner argues that “protein-type” drugs are at once envisaged from the “drug molecules”; see MPEP § 2131.02(III)) and/or (the broadest reasonable interpretation of “and/or” is disjunction) a peptide and the initial scaffold-target complex graph comprises a target graph comprising a plurality of target nodes (Kim Para [0056]: some of the amino acid positions are filled in), each representing a particular amino acid site of the target (Kim Para [0056]: some of the amino acid positions are filled in).
As to claim 37, Kim in view of Morrone teaches the method of claim 36, wherein the target graph comprises a plurality of target edges, each associated with two particular target nodes and representing a relative position (Kim Para [0123]: the edges of the graph is determined by an edge index that specifies amino acids that have physical interaction) and/or (the broadest reasonable interpretation of “and/or” is disjunction) orientation of two amino acid sites represented by the two particular target nodes (Kim Para [0072]: other interactions, such as specifying the orientation of residues with respect to one another).
As to claim 38, Kim in view of Morrone teaches the method of claim 29, wherein the machine learning model is or comprises a graph neural network (Kim Para [0135]: a deep graph neural network).
As to claim 39, Kim in view of Morrone teaches the system of claim 30, wherein the initial scaffold-target complex graph comprises a plurality of nodes and edges (Kim Para [0114]-[0114]: receiving a partially filed in protein sequence as nodes and an edge value set).
As to claim 40, Kim in view of Morrone teaches the system of claim wherein the initial scaffold-target complex graph comprises a scaffold graph representing at least a portion of the peptide backbone of the biologic (Kim Para [0114]-[0114]: receiving a partially filed in protein sequence), the scaffold graph comprising a plurality of scaffold nodes, each representing a particular amino acid site of the peptide backbone (Kim Para [0056]: the nodes of the graph represent amino acids and missing values along the sequence of the protein).
As to claim 41, Kim in view of Morrone teaches the system of claim 40, wherein a subset of the scaffold nodes are unknown interface nodes, each representing a particular amino acid interface site located in proximity to the target and having an unknown, to-be-determined amino acid side chain (Kim Para [0056]: some of the amino acid positions have undefined missing values).
As to claim 42, Kim in view of Morrone teaches the system of claim 40, wherein a subset of the scaffold nodes are known scaffold nodes, each representing a particular amino acid site having a known side chain type (Kim Para [0056]: some of the amino acid positions are filled in).
As to claim 43, Kim in view of Morrone teaches the system of claim 40, wherein the scaffold graph comprises a plurality of scaffold edges, each associated with two particular scaffold nodes and representing a relative position (Kim Para [0123]: the edges of the graph is determined by an edge index that specifies amino acids that have physical interaction) and/or (the broadest reasonable interpretation of “and/or” is disjunction) orientation of two amino acid sites represented by the two particular scaffold nodes (Kim Para [0072]: other interactions, such as specifying the orientation of residues with respect to one another).
As to claim 4, Kim in view of Morrone teaches the system of claim 30, wherein the target is or comprises a protein and/or a peptide and the initial scaffold-target complex graph comprises a target graph comprising a plurality of target nodes (Kim Para [0056]: some of the amino acid positions are filled in), each representing a particular amino acid site of the target (Kim Para [0056]: some of the amino acid positions are filled in).
As to claim 45, Kim in view of Morrone teaches the system of claim 44, wherein the target graph comprises a plurality of target edges, each associated with two particular target nodes and representing a relative position (Kim Para [0123]: the edges of the graph is determined by an edge index that specifies amino acids that have physical interaction) and/or (the broadest reasonable interpretation of “and/or” is disjunction) orientation of two amino acid sites represented by the two particular target nodes (Kim Para [0072]: other interactions, such as specifying the orientation of residues with respect to one another).
As to claim 46, Kim in view of Morrone teaches the system of claim 30, wherein the machine learning model is or comprises a graph neural network (Kim Para [0135]: a deep graph neural network).
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 29 and 30 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 Application’s claims are a genus of the species of the reference patent, with minor variations that are at once envisaged.
Instant Application (18/219,325)
Reference patent 11,742,057
29. 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 biologic;
(b) generating, by the processor, using a machine learning model, 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 biologic and/or using the predicted interface to design the amino acid interface of 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.
30. 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:
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 biologic;
(b) generate, using a machine learning model, 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 biologic and/or use the predicted interface to design the amino acid interface of the 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.
Claims 29 and 30 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 37 of copending Application No. 18/216172 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant Application’s claims are a genus of the species of the reference patent, 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 (18/219,325)
Reference Application 18/216172
29. 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 biologic;
(b) generating, by the processor, using a machine learning model, 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 biologic and/or using the predicted interface to design the amino acid interface of biologic.
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.
30. 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 biologic;
(b) generate, using a machine learning model, 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 biologic and/or use the predicted interface to design the amino acid inter
face 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240412810 A1: Pertinent intervening art claiming the benefit of provisional patent application 63/261,646 filed September 21, 2021; this is before the effective filing date of one of the provisional patent applications of the instant application, 63/353,481.
US 7751987 B1: predicting amino acid from specified 3D structure
US 20130303387 A1: See Figure 10A regarding ligands
US 20230083810 A1: See element S310a
US 20230083810 A1: graph neural networks and masked regions
Designing real novel proteins using deep graph neural networks. Alexey Strokach, David Becerra, Carles Corbi, Albert Perez-Riba, Philip M. Kim. bioRxiv 868935; doi: https://doi.org/10.1101/868935
Strokach, Alexey, et al. "Fast and flexible protein design using deep graph neural networks." Cell systems 11.4 (2020): 402-411.
US-20240038337-A1: this reference is a pre-grant publication of an application in the same patent family
US-20230034425-A1: this reference is a pre-grant publication of an application in the same patent family
US-20240412810-A1: this reference is a pre-grant publication of an application in the same patent family
US 12027235 B1: Pertinent because it is similar and has a similar Applicant
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