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 .
Priority
Instant application does claim the benefit of priority to application No. 63/248,787 filed on 9/27/2021 and given this priority date.
Drawings
The Drawings filed on 23 September 2023 are accepted.
Specification (Abstract)
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.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. (FP 6.16) (MPEP § 608.01(b))
The abstract of the disclosure is objected because inclusion of legal phraseology (i.e., “said each residue”); A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. (FP 6.16) (MPEP § 608.01(b))
The abstract of the disclosure is objected to because it is written in a manner that is overly claim-like and therefore requires revision (FP 6.13). A corrected abstract must be submitted that complies with the guidelines. The revised abstract must be presented on a separate sheet, apart from any other text, in accordance with MPEP § 608.01(b).
The abstract must not merely describe the structure of the invention, but must also indicate the technical problem addressed, the essence of the solution to that problem, and its intended use. Applicants are required to amend the abstract to properly summarize the purpose of the invention (FP 6.14), in compliance with MPEP § 608.01(b).
Status of claims
Claims 1-12 are pending and examined on the merits.
Claim 1-12 are rejected.
Claim Rejections - 35 USC § 112(b)
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 6 and 7 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.
Claim 6 recites “Relevant information that is related to”
“Relevant information that is related to”: The term "relevant" is a subjective, non-limiting term of degree. It does not provide a clear boundary to about what information is, or is not, included, leaving the scope of the claim dependent on the subjective interpretation of the user. (MPEP § 2173.02). It is unclear to one of ordinary skill in the art exactly what information is “relevant” or what constitutes a relation in this context.
Claim 7 recites “that possibly results from mutation”
The term “possibly” in claim 7 is a relative term which renders the claim indefinite. The term “possibly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The words “possibly” in claim 7 introduce ambiguity. The word “possibly” is used without clear explanation of what possibly defines in terms of side-chain dihedral angles formation. Functional language tied to possibilities must have clear criteria; “possibly results from mutation” is vague. (MPEP § 2173.02). Applicants are advised to reword the claim for clarity and resubmit it.
Claim Rejections - 35 USC § 101
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. (FP 7.04.01)
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, the instant claims 1-6, are drawn to a process (method), claims 7-12 are drawn to a system, and therefore are found to recite statutory subject matter (Step 1: YES). The instant claims are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). The instant claims recite the following limitations that equate to an abstract idea:
Claim 1 recites
Determining, based on sequence data of a plurality of strains of the specific virus, a mutation frequency”. (Mental process) The step is interpreted as a mental process because it can be performed in the human mind,
Analyzing P number of entries of protein structure data that are respectively related to P number of coronavirus spike protein-antibody complexes.” The act of analyzing data is viewed as something that can be performed in the human mind (an evaluation, judgment, or observation), therefore, falling under the mental process grouping of abstract ideas.
“Identifying, based on the interatomic distances, all contact residues in the P number of coronavirus spike protein-antibody complexes.” (Mental process) it can practically be performed in the human mind or with pen and paper. For example, comparing distances between atoms.
“Each of the contact residues is one of the residues in the wild-type coronavirus spike protein that includes an a-carbon which is spaced apart by a distance less than 5 A from another a-carbon of a residue of one of the P number of antibodies that is paired with the contact residue” (Law of nature). Claiming an antibody or epitope solely by its binding footprint, contact residues, or spatial distances relative to a wild-type (naturally occurring) viral protein recites a product of nature.
Counting a total number of contact residues, mathematical concept of a calculation and mental process
“Estimating a candidate binding free energy value of the target interface by using a pre- established model.” This requires a model to calculate binding energy. Therefore, it is considered as mental process and a mathematical concept of a calculation. (Mathematical calculation and mathematical relationship)
“Determined based on information related to properties of side-chain dihedral angles and bond rotation of amino acids”. (Mental process and mathematical concept)
Selecting a greatest one of P number of candidate binding free energy values, is a mental step and mathematical concept
“Normalizing the representative binding free energy value Blj into a normalized binding free energy value using min-max scaling.” Min-max scaling normalization is an algebraic formula (min-max scaling) and it is considered as mathematical concept.
Determining a mutation effect score based on the mutation frequency, the total number of the contact residues and the normalized binding free energy value in a manner that the mutation effect score ranges from zero to one, is a mathematical concept of a mathematical relationship and mental process
“Generating a mutation effect epitope map that is related to the mutation effect scores determined for all residues in the wild-type coronavirus spike protein.” Evaluation, comparison, or mapping of epitope residues can be performed in the human mind or by a human using a pen and paper therefore, it is considered a mental process.
Determining a region in the spike protein as a target epitope based on the epitope map, is a mental step and mathematical concept
Claim 3 recites the step of normalizing the representative binding free energy value B is to calculate the normalized binding free energy value. The step for normalization is an algebraic formula and it is considered as mathematical concept of a calculation.
Claim 4 recites the step of determining a mutation effect score Ej,1 is to calculate the mutation effect score. (Mathematical concept). The claim recites a formula for determination of mutation effect score; therefore, it is considered as mathematical concept.(Mathematical formula)
Claim 6 recites
Estimating a candidate binding free energy value: from the entry of protein structure data…… (Mental process)
Determining, based on information related to properties of side-chain dihedral angles and bond rotation of amino acids…… (Mental process)
Calculating a value of atomic-level energy and a Euclidean distance based on the spatial coordinate sets of the heavy…… (Mathematical concept)
Calculating, based on the values of atomic-level energy and the Euclidean distances thus calculated, an atomic distance related…. (Mathematical concept)
Estimating the candidate binding free energy value of the target interface by feeding, into the pre-established model…… (Mental Process)
Claim 7 recites
“Determining, based on sequence data of a plurality of strains of the specific virus, a mutation frequency”. (Mental process) The step is interpreted as a mental process because it can be performed in the human mind,
Analyzing P number of entries of protein structure data that are respectively related to P number of coronavirus spike protein-antibody complexes.” The act of analyzing data is viewed as something that can be performed in the human mind (an evaluation, judgment, or observation), therefore, falling under the mental process grouping of abstract ideas.
“Identifying, based on the interatomic distances, all contact residues in the P number of coronavirus spike protein-antibody complexes.” (Mental process) it can practically be performed in the human mind or with pen and paper. For example, comparing distances between atoms.
“Estimating a candidate binding free energy value of the target interface by using a pre- established model.” This requires a model to calculate binding energy. Therefore, it is considered as mental process.
“Normalizing the representative binding free energy value Blj into a normalized binding free energy value.” The step for normalization is an algebraic formula (min-max scaling) and it is considered as mathematical concept.
“Generating a mutation effect epitope map that is related to the mutation effect scores determined for all residues in the wild-type coronavirus spike protein.” Evaluation, comparison, or mapping of epitope residues can be performed in the human mind or by a human using a pen and paper therefore, it is considered a mental process.
Claim 9 recites a processor is configured to normalize the representative binding free energy value Bij by calculating the normalized binding free energy value as Hij; minBmax(B)-min(B) where max(B) represents a greatest one of all of the representative binding free energy values, and min(B) represents a smallest one of all of the representative binding free energy values. The step for normalization is an algebraic formula and it is considered as mathematical concept.
Claim 10 recites a processor is configured to determine the mutation effect score Eij by calculating the mutation. (Mathematical concept) Involves calculating a variable based on mutation data, it is considered as a mathematical calculation, formula, or relationship.
Claim 12 recites
Calculate spatial coordinate sets respectively of all heavy atoms ………. (Mathematical concept)
Calculate a value of atomic-level energy and a Euclidean distance…… (Mathematical concept)
Calculate, based on the values of atomic-level energy and the Euclidean distances…… (Mathematical concept)
Determine, based on information related to properties of side-chain dihedral angles…. (Mental process)
Estimate the candidate binding free energy value of the target interface…. (Mental process)
As such, claims 1-12 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Specifically, the claims recite the following additional elements:
Claim 2 recites presenting the mutation effect epitope map and the target epitope.
Claim 5 recites the pre-established model is implemented by a deep neural network (DNN).
Claim 6 recites
Obtaining spatial coordinate sets respectively of all heavy atoms of the corresponding one of the P numbers of coronavirus spike protein-antibody complexes.
Obtaining an inferred rotation angle that is related to a side chain of said each residue….
Obtaining relevant information that is related to said each residue of the wild-type coronavirus spike protein
Claim 7-12 recites a computing system with processor, storage device, input and output device.
Claim 8 recites presenting the mutation effect epitope map and the target epitope.
Claim 11 recites the pre-established model is implemented by a deep neural network (DNN).
Claim 12 recites a storage device configured to store amino acid structure data.
The limitations for presenting the mutation effect epitope map, and presenting the results using a display device serves as being merely an insignificant, routine, or conventional post-solution activity and used an input for the judicial exception. Therefore, these limitations are mere data gathering or analyzing activities and displaying the results using a conventional display system. As set forth in MPEP 2106.05(g), mere data gathering and analyzing activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
There are no limitations that indicate that the DNN, processor, storage, input and output device are anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-12 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, routine and conventional activity.
As discussed above, there are no additional limitations to indicate that the claimed processor requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity, as evidenced by Matthew Ragoza et al. (Chem Inf Model. 2017 April 24; 57(4): 942–957) and Gomes, J., Ramsundar et al. (Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity) articles. Matthew Ragoza et al. (abstract) and Gomes, J., Ramsundar et al.(abstract) in their article discloses the use of DNN (deep neural network) in protein binding affinity research, where they discuss calculation of binding score between protein and ligand complexes. As such, the combination of additional elements recited in the claims is well-understood, routine and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-12 are not patent eligible.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim 1, 2, 3, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. in view of DiMaio et al., in view of Krivov et al., in view of Benhar et al.
Regarding claim 1, Chen et. al teaches mutation frequency (Page 6932, Column 1 and 2, middle), where he generated a real-time interactive SARS-CoV-2 Mutation Tracker to
report unique single mutations along with their mutation frequency on SARS-CoV-2 using GISAID database, suggesting the limitations of determining, based on sequence data of a plurality of strains of the specific virus, a mutation frequency.
Chen et. al also discloses:
Spike protein – antibody complex based on analyzing dozens of complex spike-ab complexes (Page 6933, column 2, middle) (page 6929, top), suggesting the claimed limitation of analyzing P number of entries of protein structure data that are respectively related to P number of coronavirus spike protein-antibody complexes.
Binding free energy (Page 6931, Column 2, middle) suggesting the claimed limitation of mutation induced binding free energy.
Antigenic epitope (escape mutation) (Page 6936, Column 1, top) (Page 6943, Column 2, middle) (page 6944, column 2, top) reads to claimed limitation of epitope map.
Deep learning method to predict binding free energy (Page 6945, column 1, bottom) (page 6931, column 2, middle) suggesting claimed limitation of pre-established model.
Contact residues between antibody and spike protein (Page 6943, Column 2, middle) (Fig 4) suggesting limitation of identifying, based on the interatomic distances, all contact residues in the P number of coronavirus spike protein-antibody complexe.,
Identification of escape mutation and mutation hotspot by combination of mutation frequency/occurrence data (from 200k viral genome) (Page 6936-6939) suggesting the claimed limitation of epitope map.
Binding free energy (Page 6936-6939)suggesting the claimed limitation of calculation of binding free energy.
Structural information and ML (machine learning) model. (Page 6936-6939) suggesting the claimed limitation of pre-established model.
However, Chen et.al. (2021) does not teach normalization of binding free energy using min-max scaling approach. Also, he does not teach or disclose spatial coordinates of heavy atoms, generation of new atomic coordinates due to introduction of mutated residues, prediction of side chain conformation that uses dihedral angles and bond rotation to model mutations, protein interface atomic level interaction between residues using interatomic distances in his work.
DiMaio et al. teaches a computational framework for modeling protein structure and protein-protein interaction, using protein structure data comprising spatial coordinate ( pg. 2 column 2, middle(method), pg. 13, column 2, top), reads to claim limitation of mutant residue that is determined based on information related to properties of side-chain dihedral angles and bond rotation of amino acids, introduction of mutations into residues of a protein structures and generate new atomic coordinates (pg. 3 column 1, middle) (pg. 3 column 1, middle) suggesting the limitation of to obtain a plurality of interatomic distances respectively between a plurality of heavy-atom pairs, each of the heavy-atom pairs including two heavy atoms that respectively. and estimation of binding free energy change, (Delta G) for protein-protein interfaces, which suggest the limitations estimating a candidate binding free energy value of the target interface.
However, DiMaio et al. does not teach prediction of protein side chain conformation that uses dihedral angles.
Krivov et al. (2009) teaches prediction of protein side-chain conformation using backbone dependent rotamer libraries, uses dihedral angles and bond rotation to model mutations (pg. 1, Abstract) which suggest the limitations of determining properties of side-chain dihedral angles and bond rotation of amino acid.
However, Krivov et al. does not teach scaling approach to normalize data that uses min-max scaling.
Benhar et al. provides a min-max scaling approach as a conventional approach routinely used to normalize data specifically for binding energy in protein-protein complex. In computer science the following website Max Normalization - an overview | ScienceDirect Topics shows the formula of min-max scaling in Role of Max-Normalization in Machine Learning and Data Mining and H. Benhar et al. (2020) describe the use of min-max scaling is very common in biomedical field (pg. 11, column 11, bottom; pg. 15, column 2, middle), which suggests the limitation of normalizing the representative binding free energy value Blj into a normalized binding free energy value Hid by using min-max scaling.
A person having ordinary skill in the art (PHOSITA) would have been motivated by the combined teachings of Chen et al., DiMaio et al., Krivov et al., and Benhar et al. to generate a mutation-tolerable epitope map.
This approach relies on established methodologies such as atomic-level modeling and rotamer libraries, taught by DiMario et al. and Krivov et al. respectively and high-throughput screening (Chen et al.) to address the recognized challenge of designing mutation-resistant vaccines. Furthermore, it yields predictable outcomes with a strong expectation of success.
The goal of creating a mutation-resistant vaccine necessitates mapping “tolerable” mutations. A PHOSITA faced with this challenge would have a finite number of recognized methods to generate this map, specifically combining viral genomics/binding frequency (Chen et al.) with structural modeling (DiMaio et al.), optimization of modeling mutation (Krivov et al.) and conventional normalization technique. (Benhar et al.)
The combination is a “predictable variation” because Chen provides the data, while DiMaio/Krivov provide established computational tools to interpret it, and Benhar teaches normalization, leading to a high expectation of success in generating a precise epitope map.
The references are in the same or analogous fields (structural biology, genomics, vaccine design). Combining Chen (genomic data), DiMaio (modeling), and Krivov (rotamer refinement) is a combination of known elements, each acting in its expected manner. The resulting pipeline provides a predictable result to better mapping of mutation tolerance improving upon existing antibody studies.
Krivov's side-chain prediction is a "known technique" meant for improving structural modeling, and DiMaio's atomic-level modeling is standard for epitope mapping. Using these to optimize Chen's high-throughput findings is the logical application of known tools to a new, yet similar, problem.
A PHOSITA would have been motivated to combine these to address the well-known need in the prior art for better, mutation-resistant vaccine design, using standard techniques to improve epitope prediction accuracy.
The desire for "mutation resistant vaccines" (found in prior art) provides the reason to combine Chen (genomics) with DiMaio/Krivov (structural modeling) and Benhar (normalization). A person of ordinary skill (PHOSITA) would easily combine these elements to improve epitope prediction, making it a logical next step for generating mutation tolerable vaccine.
Regarding claim 2, Chen et al. teaches and present antigenic epitope map (escape mutation) (pg. 6936, Column 1, top) (pg. 6943, Column 2, middle) which suggests limitations of generating a mutation effect epitope and target epitope.
Concerning claim 3, Chen et al. teaches the estimation of binding free energy. The formula that was provided in claim 1 for normalization of binding free energy (min-max scaling method) is a conventional normalization method that is routinely used in biomedical field (Benhar et. al) which suggest limitation of normalizing binding free energy value.
Pertaining claim 5, Chen et. al. teaches deep learning method that were used to calculate binding free energy. (pg. 6945, column 1, bottom) (pg. 6931, column 2, middle) Deep neural network is a conventional ML algorithm that is regularly used to train a model by providing relative variables to predict a desired outcome.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. in view of DiMaio et al., in view of Krivov et al., in view of Benhar et al. as applied to claims 1,2,3, and 5 above, and further in view of Dunbrack et al. in view of Kortemme & Baker et al.
Chen et al. DiMaio et al., Krivov et al., and Benhar et al. are applied to claims 1, 2, 3 and 5, as above.
Regarding claim 6, DiMaio et al. teaches a computational framework for modeling protein structure and protein-protein interaction, including antibody-antigen complexes using protein structure data comprising spatial coordinate, sets of atoms for protein complexes (including heavy atoms) (pg. 1 column 2, middle(method), pg. 13, column2, top) which suggests the limitation of calculating spatial coordinate sets respectively of all heavy atoms of the mutant residue based on the spatial coordinate sets of all heavy atoms of said each residue of the wild-type coronavirus spike protein and the inferred rotation angle,, introduction of mutations into residues of a protein structures and generate new atomic coordinates (pg. 3 column 1, middle) suggesting the limitation of calculation of atomic-level energy and calculation of pairwise atomic interaction energies and distance-dependent energy terms (pg. 8 column 1, top) and estimation of binding free energy change, (Delta G ) for protein-protein interfaces suggesting the limitations of determining atomic interaction force of the target interface.
However, DiMaio does not explicitly teach the fundamental biological and thermodynamic mechanisms of protein folding that involves protein side chain conformation requiring the use of dihedral angles.
Krivov et al. (2009) teaches prediction of protein side-chain conformation using backbone dependent rotamer libraries, uses dihedral angles and bond rotation to model mutations and computes spatial coordinate of heavy atoms of mutated residues. (pg. 1, Abstract) which suggests the limitation of determining properties of side-chain dihedral angles and bond rotation of amino acid.
A PHOSITA would be motivated in combining DiMaio’s computational framework for protein structure modeling and binding free energy calculations with Krivov’s method of using backbone-dependent rotamer libraries and dihedral angles for side-chain conformation prediction. A person of ordinary skill in the art would be motivated to combine these teachings to accurately generate atomic coordinates and determine side-chain properties.
Krivov et al. does not explicitly teach use of side-chain dihedral angle and basis for predicting mutant residue conformations
Dunbrack et al. teaches backbone dependent rotamer libraries that provide statistical distribution of side-chain dihedral angle and basis for predicting mutant residue conformations which suggests the limitation of obtaining an inferred rotation angle that is related to a side chain of said each residue of the wild-type coronavirus spike protein from amino acid structure data that contains information related to properties of backbone dihedral angles, side-chain dihedral angles and bond rotation of amino acids.
Dunbrack et al. does not teach calculation of atomic level interaction energies between residues using interatomic distances, and interaction forces.
Kortemme and Baker et al. teach computational alanine scanning and protein interface analysis where he shows calculation of atomic level interaction energies between residues using interatomic distances, determining interaction forces and energetic contribution at protein-protein interfaces pg. 2, column 1, middle; pg. 1, column 1, middle) which suggests the limitation of estimating an atomic interaction force of the target interface.
A person of ordinary skill in the art (PHOSITA) in the field of computational biology would have found it obvious to combine the disclosed teachings because:
DiMaio provides a framework for antibody-antigen modeling but requires specific, accurate methods for handling mutated residues and predicting binding energy changes. Krivov and Dunbrack provide standard, widely used backbone-dependent rotamer libraries for side-chain prediction. Kortemme provides established methods for alanine scanning to compute interaction energies. Combining these to calculate interface (Delta G) would produce predictable results regarding protein stability and binding affinity.
The number of rotamer conformations is finite, and the energy calculation methods taught by Kortemme are well-understood in the art, making the application of these techniques to DiMaio’s framework is a routine optimization.
DiMaio utilizes protein structure data comprising spatial coordinates (heavy atoms) to generate computational framework for protein structure/interaction (Antibody-Antigen). Krivov et al. teach modeling mutations using rotamer libraries, resulting in updated heavy atom coordinates. Dunbrack et al. explicitly provides these libraries to predict mutant residue conformations. Kortemme and Baker teach calculating atomic interaction energies, including interatomic distances and interaction forces at interfaces. Krivov et al. teach modeling mutations using rotamer libraries, resulting in updated heavy atom coordinates. Kortemme disclose computing binding free energy changes for protein-protein interfaces.
The combination of DiMaio (framework), Krivov/Dunbrack (side-chain/mutation modeling), and Kortemme (energetic analysis) provides all elements of the claimed invention. A POSITA would have combined these to improve the accuracy of protein-protein interaction modeling, resulting in a predictable and successful optimization of existing pipeline. (MPEP § 2141).
It would be an expectation of success of using all these cited arts together because they are all in the same technology (Proteomics), and addressing the fidelity of protein-protein interactions.
Claim 7, 8, 9, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. in view of DiMaio et al., Krivov et al., Benhar et al., Dunbrack et al. and Kortemme & Baker et al.
for claims 1,2,3,5, and 6 above, and further in view of Asadi et al.
Chen et al. in view of DiMaio et al., Krivov et al., Benhar et al., Dunbrack et al. and further in view of Kortemme & Baker et al. as applied to claims 1, 2, 3, 5 and 6.
Regarding claim 7-9, 11 and 12, where claim 7 is a system claim with new limitation with a processor for the performing the series of steps described in method claim 1, storage and input (for receiving and storing data) and output device (to display results). All functional processing steps are recited here in claim 7-9, 11 and 12 are identical as to the method claims 1-6 except inclusion of a storage device to store pre-establish model, an input device for receiving sequence of specific virus, antibody data, physiochemical data and a processor for calculation of each subsequent steps to identify target epitope.
Chen et al. in view of DiMaio et al., Krivov et al., Benhar et al., Dunbrack et al. and further in view of Kortemme & Baker et al. does not teach a storage device to store data and a processor to process series of functions (in claims 1-6 and 7-12) and output device.
Asadi et al. teaches a storage device [paragraph 0080] and in this case could be any type of memory hardware that allows to store data specially in a computing environment where bioinformatic pipeline are routinely executed. In a computing era, having a storage device to store data merely adds to novelty of the invention and is well established conventional approach to use in bioinformatics. Same applies for input module and processor, [paragraph 0089] these are conventional use in a computing environment to receive data and perform calculation. And they don’t add anything new to the claim invention.
Carrying out the steps of receiving data using an input device, analysis and execution of the functional steps (described in claim one to six) with a processor, and displaying the results using an output device, it is inherent that to perform a series of complex calculation requires a processor to be carried out and also is inherent that use of a conventional input device and storage system is a routine process to receive and store data. Therefore, claim 7 is rejected under 35 U.S.C 103 as being unpatentable as applied for claim 1 in view of teaching of Asadi et al.
It would be an expectation of success of using all these cited prior arts together because they are all in the same technology (Proteomics), and addressing the fidelity issue of protein-protein interactions.
Claim 8 and 9 merely limits claim 7 wherein claim 8 states that a processor performs normalization of the representative binding free energy and in claim 9, a processor performs the calculation to estimate mutation effect score with same teaching of claim 1, 2, 3, 5, and 6 of Chen et al., Frank DiMaio et al., Krivov et al., Dunbrack et al., Kortemme & Baker et al., Benhar et al. and further teaching of Asadi et al. of computing system with a processor used for sequencing analysis.
Regarding claim 11, the claim is a mirror image of the claim 5 of the invention except for the computing system which is taught by Asadi et al.. Therefore, for a POSITA at the time of effective filing date of the claimed invention would have been obvious from teaching by Chen et al., Frank DiMaio et al., Krivov et al., Dunbrack et al., Kortemme & Baker et al., Benhar et al. (as described for claim 1,2,3,5, and 6) and further teaching of Asadi et al. of using computing system to predict binding energy of protein-protein interaction.
Regarding claim 12, the claim is a mirror image of the claim 6 of the invention except for the computing system with storage which is taught by Asadi et al. In order to store data and perform series of function it would have been obvious for a POSITA to use a conventional computing device taught by Asadi et al. which has input device (such as a keyboard) to receive data, a storage device (such as RAM) to store data, a processor for processing the steps in the method claim and output device to display the results.
Claim 4 is free of the art. Regarding claim 4, where applicant discusses how the mutation score is calculated based on mutation frequency, contact residue and physiochemical properties of contact residues. PolyPhen-2 (Polymorphism Phenotyping v2) are a widely used in bioinformatics tools, available via a web server (PolyPhen-2: prediction of functional effects of human nsSNPs, SIFT - Predict effects of nonsynonymous / missense variants) that predicts the potential impact of amino acid substitutions on human protein structure and function. It uses sequence- and structure-based features to classify missense mutations as damaging, or benign. Even though these methods allow estimation of mutation score, no publication shows the estimation of mutation effect score using same formula or formula that covers similar parameter before the effective filing date of the claimed invention.
Claim 10 is free of the art. Claim 10 recites a processor that calculates an estimated mutation effect score using the specific formula described therein. Although its functional limitations mirror those of claim 4, claim 10 is currently objected to and rejected over prior art solely due to its dependency on claim 7.
Conclusion
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/AK/Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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