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 .
Status of the Claims
Claims 1-19 are currently pending and under exam herein.
Claims 1-19 are rejected.
Priority
The instant application claims priority from foreign application IL278394 filed on 10/29/2026 and is a continuation of PCT/IL2021/051275 filed on 0/17/2021. Thus, the earliest effective filing date of the instant application is 10/29/2026.
Drawings
The Drawings filed on 10/12/2023 and 04/27/2023 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/11/2023 and 05/23/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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.
Claim 19 recites the limitation " ML model created as in claim 1". However, claim 1 does not create an ML model. Examiner believes applicant meant claim 2 which creates an ML model. There is insufficient antecedent basis for this limitation in the claim. Therefore, claim 19 is rejected.
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.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-19 are directed to mass spectrometry and machine learning methods to find antigens of interest.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a plurality of combination, each combination including a respective amino acid sequence selected from the reference sequence dataset and at least one modification selected from the variable modification dataset (mental process)
searching using a plurality of processors connected in parallel, wherein each processor searches for a respective spectra element on the plurality of combinations to identify a plurality of best peptide to spectra matches (PSMs), wherein each respective processor assigns a ranking score to respective PSM according to the respective search performed by the respective processor; (mental process, mathematical concept)
aggregating the plurality of PSMs from the plurality of processors connected in parallel to generate a main PSM list with main ranking score by computing the main ranking score from the ranking score of each respective PSM of each respective search; (mental process, mathematical concept)
selecting highest ranking PSMs according to respective main ranking scores (mental process)
storing in a modified sequence dataset, a plurality of modified sequences each including the PTM and sequences corresponding to the selected highest ranking PSMs, wherein the modified sequence dataset stores an indication of binding motifs defined by a plurality of identified PTM and corresponding sequence; (mental process, mathematical concept)
providing the modified sequence dataset for selecting a certain binding motif having a certain PTM and corresponding amino acid sequence from the modified sequence dataset capable of specifically binding an MHC presented peptide for treatment of the target disease (mental process)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset, each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; and (mathematical concept)
training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model, and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (mathematical concept)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
merging the respective set of PSM of each respective processor to create a PSM aggregation dataset wherein the highest ranking PSMs are selected from the PSM aggregation dataset.
(mental process)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein statistical parameters used in a subsequent false discovery rate (FDR) calculation are distorted by a plurality of searches of a same reference dataset over different software instances executed by the plurality of processors, and wherein merging further comprises: (mathematical concept)
removing duplicated PSM from the PSM aggregation dataset by using unmodified hits combined histogram to evaluate a number of duplicated PSM and identify the duplicated PSM for removal thereof, and (mathematical concept)
recalculating an expectation based on a restored score histogram for each PSM. (mental process)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
computing a plurality of quality assignment measures, and performing the following using the quality assignment measures: (mathematical concept)
validating the PTM of each member of the PSM aggregation dataset according to the quality measures; (mathematical concept, mental process)
filtering ambiguous assignments and isobaric decoys of the PSM aggregation dataset according to a filtering threshold; (mathematical concept)
ranking members of the PSM aggregation dataset; and (mathematical concept, mental process)
selecting the highest ranking PSMs according to the highest ranked member of the PSM aggregation dataset. (mental process)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
computing a probability score indicative of match accuracy for each PSM, wherein the highest ranking PSMs are selected according to highest probability. (mathematical concept)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
dividing the PSM aggregation dataset into groups including: unmodified, standard search modification types, and other modification types, using a threshold cutoff based on respective abundance in the PSM aggregation dataset; for each group the PSM are sorted by probability score and a threshold is set for assuring false identification is below the FDR limits. (mathematical concept)
Dependent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
when a difference in probability scores is below a defined percentage of the average probability score, the lower-ranked PSM are obtained and added to the modified sequence dataset. (mathematical concept)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a certain PSM is identified as the highest ranking PSMs when the certain PSM is identified as having a highest probability score in one respective set of PSM and a lower ranked probability score in another respective set of PSM. (mathematical concept)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
extracting the peaks from the PSM; for each peak, computing a plurality of theoretical fragment ions for an unmodified version of the respective peptide and adjust each theoretical fragment ion according to the modification mass shift, and annotating the respective peak with the theoretical fragment ions. (mental process)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the plurality of theoretical fragment ions includes a, b, y precursor and diagnostic ions with potential ammonium and water lost in expected peptide charges. (mental process)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
for each PSM, searching for modification reporter ions, providing a number of b and y ions, and computing a proportion of ion current (PIC),wherein unassigned peaks with significant intensity indicate a discrepancy between an observed spectrum defined by the respective spectra element of the plurality of PSMs and a matched peptide of the PSM (mental process)
Dependent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
for each PTM of each PSM, creating a window of potential site positions based on the annotated peaks, wherein at least one of: (i) including alternative site positions within the window, and (ii) including alternative combinations of modifications with equivalent mass (mental process)
Dependent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
searching for identical masses or combination of masses that match the respective PTM mass shift indicative of mass decoy and/or isobaric masses, and in response to finding the identical masses or combination of masses, removing the ambiguous respective identified PSM corresponding to the respective PTM. (mental process, mathematical concept)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
further comprising excluding PSM with total peptide mass greater than average mass of a maximum peptide length plus a tolerance value (mental process, mathematical concept)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
further comprising, for each respective PSM, searching in a dataset of known PSM of healthy cells and cells with the target disease for a match, and increasing likelihood of the respective PSM being included in the modified sequence dataset when the PSM is found in the dataset of known PSM (mental process, mathematical concept)
Dependent claim XXXX recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset, each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, the modified sequence dataset created as in claim 1, each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; and (mathematical concept)
training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model, and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (mathematical concept)
Independent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
feeding the input into an ML model created as in claim 1; and (mathematical concept)
obtaining as an outcome of the ML model, for the input of (i) an indication of whether the certain modified sequence is predicted to fit a motif that binds to a cell of the MHC type, and for the input of (ii) obtaining at least one motif predicted to be created from the full protein length and PTMs. (mathematical concept)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-19 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A computer implemented method for generating a dataset of post translations modifications (PTM) on major histocompatibility complex (MHC) bound peptides, comprising:
receiving a mass spectrometry (MS) dataset obtained from a sample of cells associated with a target disease for treatment, the MS dataset storing a plurality of spectra data elements outputted by a MS device analyzing MHC bound peptides to generate a plurality of amino acid sequences, each spectra data element for a respective amino acid sequence of the MHC bound peptides;
receiving a reference sequence dataset storing amino acid sequences of proteins;
receiving a variable modification dataset storing a plurality of modifications each including a respective amino acid and expected mast shift;
The additional element in dependent claim 3 includes:
wherein at least one of: the modified sequence dataset stores peptides selected from the group consisting of SEQ ID NO: 1-10746, 10817, 10819, 10820, 10823, 10824, 10826 and 10827, the target disease comprises cancer, and the certain binding motif is selected for treating the cancer using immunotherapy, and the MHC comprises HLA I. (this just limits the data gathered)
The additional element in dependent claim 4 includes:
allocating a respective subset of the plurality of combinations to a plurality of processors connected for parallel processing, each respective processors searching the respective spectra element on the respective subset to identify a respective set of PSM,
The additional element in independent claim 19 includes:
receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs
The additional elements of receiving a mass spectrometry (MS) dataset obtained from a sample of cells associated with a target disease for treatment, the MS dataset storing a plurality of spectra data elements outputted by a MS device analyzing MHC bound peptides to generate a plurality of amino acid sequences, each spectra data element for a respective amino acid sequence of the MHC bound peptides (Claim 1), receiving a reference sequence dataset storing amino acid sequences of proteins (Claim 1), receiving a variable modification dataset storing a plurality of modifications each including a respective amino acid and expected mast shift (Claim 1), wherein at least one of: the modified sequence dataset stores peptides selected from the group consisting of SEQ ID NO: 1-10746, 10817, 10819, 10820, 10823, 10824, 10826 and 10827, the target disease comprises cancer, and the certain binding motif is selected for treating the cancer using immunotherapy, and the MHC comprises HLA I (Claim 3, this just limits the data gathered) receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs (claim 19) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of a computer implemented method for generating a dataset of post translations modifications (PTM) on major histocompatibility complex (MHC) bound peptides, comprising (Claim 1), allocating a respective subset of the plurality of combinations to a plurality of processors connected for parallel processing, each respective processors searching the respective spectra element on the respective subset to identify a respective set of PSM (claim 4) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-19 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-19 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1-19 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of receiving a mass spectrometry (MS) dataset obtained from a sample of cells associated with a target disease for treatment, the MS dataset storing a plurality of spectra data elements outputted by a MS device analyzing MHC bound peptides to generate a plurality of amino acid sequences, each spectra data element for a respective amino acid sequence of the MHC bound peptides (Claim 1), receiving a reference sequence dataset storing amino acid sequences of proteins (Claim 1), receiving a variable modification dataset storing a plurality of modifications each including a respective amino acid and expected mast shift (Claim 1), wherein at least one of: the modified sequence dataset stores peptides selected from the group consisting of SEQ ID NO: 1-10746, 10817, 10819, 10820, 10823, 10824, 10826 and 10827, the target disease comprises cancer, and the certain binding motif is selected for treating the cancer using immunotherapy, and the MHC comprises HLA I (Claim 3, this just limits the data gathered) receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs (claim 19) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by Kong et al., Bassani-Sternberg et al, Cox et al. which show that the use of mass spectrometry to determine post translational modifications was routine. There were a variety of computational tools developed in order to deal with this problem specifically showing it was a conventional data gathering step performed in many labs.
The additional elements of a computer implemented method for generating a dataset of post translations modifications (PTM) on major histocompatibility complex (MHC) bound peptides, comprising (Claim 1), allocating a respective subset of the plurality of combinations to a plurality of processors connected for parallel processing, each respective processors searching the respective spectra element on the respective subset to identify a respective set of PSM (claim 4) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Therefore, when taken alone, all additional elements in claims 1-19 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-19 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
Claims 1, 3, 8, 9, 10, 11, 12, 14, 15, 16, 17, are rejected under 35 U.S.C. 103 as being unpatentable over Kong et al. (Kong, A. T.; Leprevost, F. V.; Avtonomov, D. M.; Mellacheruvu, D.; Nesvizhskii, A. I. MSFragger: Ultrafast and Comprehensive Peptide Identification in Mass Spectrometry–Based Proteomics. Nature Methods 2017, 14 (5), 513–520) in view of Bassani-Sternberg et al (Bassani-Sternberg, M.; Pletscher-Frankild, S.; Juhl Jensen, L.; Mann, M. Mass Spectrometry of Human Leukocyte Antigen Class I Peptidomes Reveals Strong Effects of Protein Abundance and Turnover on Antigen Presentation. Molecular & Cellular Proteomics 2021, 14 (3), 658–673.) in further view of Cox et al. (Cox, J.; Neuhauser, N.; Michalski, A.; Scheltema, R. A.; Olsen, J. V.; Mann, M. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment. Journal of Proteome Research 2011, 10 (4), 1794–1805) in further view of Bjornson et al. (Bjornson, R. D.; Carriero, N. J.; Colangelo, C.; Shifman, M.; Cheung, K.-H.; Miller, P. L.; Williams, K. X!!Tandem, an Improved Method for Running X!Tandem in Parallel on Collections of Commodity Computers. Journal of Proteome Research 2007, 7 (1), 293–299.) in further view of Shteynberg et al. (iProphet: Multi-Level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates. Molecular & Cellular Proteomics 2011, 10 (12), M111.007690.) in further view of Cobbold et al. (MHC Class I–Associated Phosphopeptides Are the Targets of Memory-Like Immunity in Leukemia. Science Translational Medicine 2013, 5 (203).). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, 8, 10, 11, 12, 14, 15, 16, Kong et al. present a fragment-ion indexing method, and its implementation in peptide identification tool msFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. using several large proteomic data sets, we demonstrate how msFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein–RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics. MSFragger enables a wide range of analyses beyond interrogation of unlabeled proteomes. We performed open searches using spectra from labeling-based experiments (for example, SILAC or tandem mass tag) by specifying the labeled amino acids as a variable modification, thus allowing quantitative comparison of the modification states of proteins (abstract, A computer implemented method for generating a dataset of post translations modifications (PTM) on major histocompatibility complex (MHC) bound peptides, comprising (Claim 1) generating a plurality of combination, each combination including a respective amino acid sequence selected from the reference sequence dataset and at least one modification selected from the variable modification dataset; (Claim 1), further comprising: for each PTM of each PSM, creating a window of potential site positions based on the annotated peaks, wherein at least one of: (i) including alternative site positions within the window, and (ii) including alternative combinations of modifications with equivalent mass (Claim 14), wherein for each respective PTM of each identified PSM: searching for identical masses or combination of masses that match the respective PTM mass shift indicative of mass decoy and/or isobaric masses, and in response to finding the identical masses or combination of masses, removing the ambiguous respective identified PSM corresponding to the respective PTM (Claim 15) further comprising excluding PSM with total peptide mass greater than average mass of a maximum peptide length plus a tolerance value (claim 16)
With respect to the limitations of Claims 11, 12, Kong et al. teaches MSFragger digests a protein database and generates a nonredundant set of peptides that are arranged in an index, which is then used to generate a fragment index for efficient and simultaneous scoring of an experimental spectrum against all candidate spectra. (c) Mass binning and precursor mass ordering in the fragment index allows rapid retrieval of candidate spectra that match a given experimental fragment ion. Scores of candidate peptides corresponding to retrieved spectra are incremented. (Figure 1 caption) the mass offset within the bin, the charge state, and the fragment-ion identity. MSFragger begins by performing an in silico digestion of the protein database (Fig. 1a). It then removes redundant peptides and orders them by their theoretical mass (including any modified peptides generated as a result of variable modifications), (further comprising: extracting the peaks from the PSM; for each peak, computing a plurality of theoretical fragment ions for an unmodified version of the respective peptide and adjust each theoretical fragment ion according to the modification mass shift, and annotating the respective peak with the theoretical fragment ions. (Claim 11) Figure 3 shows the loss of ammonium in calculating theoretical mass (wherein the plurality of theoretical fragment ions includes a, b, y precursor and diagnostic ions with potential ammonium and water lost in expected peptide charges (Claim 12),
Kong et al. does not explicitly teach
providing the modified sequence dataset for selecting a certain binding motif having a certain PTM and corresponding amino acid sequence from the modified sequence dataset capable of specifically binding an MHC presented peptide for treatment of the target disease (Claim 1) receiving a mass spectrometry (MS) dataset obtained from a sample of cells associated with a target disease for treatment, the MS dataset storing a plurality of spectra data elements outputted by a MS device analyzing MHC bound peptides to generate a plurality of amino acid sequences, each spectra data element for a respective amino acid sequence of the MHC bound peptides (Claim 1),
wherein at least one of: the modified sequence dataset stores peptides selected from the group consisting of SEQ ID NO: 1-10746, 10817, 10819, 10820, 10823, 10824, 10826 and 10827, the target disease comprises cancer, and the certain binding motif is selected for treating the cancer using immunotherapy, and the MHC comprises HLA I (Claim 3)
receiving a reference sequence dataset storing amino acid sequences of proteins (Claim 1)
receiving a variable modification dataset storing a plurality of modifications each including a respective amino acid and expected mast shift (Claim 1),
dividing the PSM aggregation dataset into groups including: unmodified, standard search modification types, and other modification types, using a threshold cutoff based on respective abundance in the PSM aggregation dataset; for each group the PSM are sorted by probability score and a threshold is set for assuring false identification is below the FDR limits (Claim 8)
wherein a certain PSM is identified as the highest ranking PSMs when the certain PSM is identified as having a highest probability score in one respective set of PSM and a lower ranked probability score in another respective set of PSM (Claim 10)
searching using a plurality of processors connected in parallel, wherein each processor searches for a respective spectra element on the plurality of combinations to identify a plurality of best peptide to spectra matches (PSMs), wherein each respective processor assigns a ranking score to respective PSM according to the respective search performed by the respective processor (Claim 1))
aggregating the plurality of PSMs from the plurality of processors connected in parallel to generate a main PSM list with main ranking score by computing the main ranking score from the ranking score of each respective PSM of each respective search; selecting highest ranking PSMs according to respective main ranking scores (Claim 1)
when a difference in probability scores is below a defined percentage of the average probability score, the lower-ranked PSM are obtained and added to the modified sequence dataset (claim 9)
providing the modified sequence dataset for selecting a certain binding motif having a certain PTM and corresponding amino acid sequence from the modified sequence dataset capable of specifically binding an MHC presented peptide for treatment of the target disease (Claim 1)
storing in a modified sequence dataset, a plurality of modified sequences each including the PTM and sequences corresponding to the selected highest ranking PSMs, wherein the modified sequence dataset stores an indication of binding motifs defined by a plurality of identified PTM and corresponding sequence; and (Claim 1)
With respect to the limitations of Claims 1, 3, 17, Bassani-Sternberg et al. teaches present a high-throughput mass-spectrometry-based workflow that allows stringent and accurate identification of thousands of such peptides and direct determination of binding motifs. Applying the workflow to seven cancer cell lines and primary cells, yielded more than 22,000 unique HLA peptides across different allelic binding specificities. (abstract, receiving a mass spectrometry (MS) dataset obtained from a sample of cells associated with a target disease for treatment, the MS dataset storing a plurality of spectra data elements outputted by a MS device analyzing MHC bound peptides to generate a plurality of amino acid sequences, each spectra data element for a respective amino acid sequence of the MHC bound peptides (Claim 1), wherein at least one of: the modified sequence dataset stores peptides selected from the group consisting of SEQ ID NO: 1-10746, 10817, 10819, 10820, 10823, 10824, 10826 and 10827, the target disease comprises cancer, and the certain binding motif is selected for treating the cancer using immunotherapy, and the MHC comprises HLA I (Claim 3), this is a Markush style grouping and only requires the MHC comprises HLA I to be met in order to satisfy the claim.)
With respect to the limitations of Claims 1, 8 , 10, Cox et al. teaches a key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Andromeda,anovelpeptidesearchengineusing a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform. The combi nation enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. We demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, signifi cantly improving the total number of identified peptides (abstract) False discovery rates for the same initial probability score can still depend on the number of modifications and on the mass of the peptide. Andromeda (pg. 1803), receiving a reference sequence dataset storing amino acid sequences of proteins (Claim 1) Cox et al. also teaches oxidation of methionine and N-terminal protein acetylation were used as variable modifications for all searches. A mass tolerance of 6 ppm was used for the peptide mass. (pg. 1795, col. 2, paragraph 4, receiving a variable modification dataset storing a plurality of modifications each including a respective amino acid and expected mast shift (Claim 1), dividing the PSM aggregation dataset into groups including: unmodified, standard search modification types, and other modification types, using a threshold cutoff based on respective abundance in the PSM aggregation dataset; for each group the PSM are sorted by probability score and a threshold is set for assuring false identification is below the FDR limits (Claim 8) wherein a certain PSM is identified as the highest ranking PSMs when the certain PSM is identified as having a highest probability score in one respective set of PSM and a lower ranked probability score in another respective set of PSM (Claim 10), ) generating a plurality of combination, each combination including a respective amino acid sequence selected from the reference sequence dataset and at least one modification selected from the variable modification dataset; (Claim 1)
With respect to the limitations of Claims 1, Bjornson et al. teaches new mass spectrometers, which are generating greater numbers of high-quality spectra in a shorter period of time, along with intensified interest in post-translational modification of proteins, are imposing significantly greater demands on protein identification software. We have addressed this need by implementing a simple, efficient, distributed-memory parallelized version of X!Tandem. By employing a low-impact Owner Computes technique, we were able to create X!!Tandem, a parallel version of X!Tandem that demonstrated excellent speedup on an example data set, reducing a computation that took 10 h on a single CPU to 21 min on 64 CPUs, a nearly 29-fold speedup. In addition, it is substantially the same as the original code, is run in the same manner, and produces identical output. Because the code is parallelized using a standard message-passing library, MPI, it can be run on comparatively inexpensive networks or clusters of commodity processors. The source code has been made freely available via the same open-source license as X!Tandem. Beyond this particular program, the authors have found the Owner Computes technique used to parallelize X!Tandem to be extremely useful for complex codes that are not otherwise easily parallelized. (pg. 298, col. 2, paragraph 2,searching using a plurality of processors connected in parallel, wherein each processor searches for a respective spectra element on the plurality of combinations to identify a plurality of best peptide to spectra matches (PSMs), wherein each respective processor assigns a ranking score to respective PSM according to the respective search performed by the respective processor (Claim 1))
With respect to the limitations of Claims 1, 9, Shteynberg et al. teaches iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. FDR is a way to rank the accuracy of identification which inherently tells the most reliable proteins. A person of ordinary skill in the art would know to take the lowest FDR scores as the most reliable matches. (abstract) and PeptideProphet calculates the posterior probability of a correct PSM, individually for each search engine output. iProphet combines multiple lines of evidence and computes accurate probabilities at the level of unique peptide sequences, assisted by the introduction of new grouping variables, Using a probability to select the best ones and remove low ones is an obvious modification of a person of ordinary skill in the art (pg. 7 final paragraph, aggregating the plurality of PSMs from the plurality of processors connected in parallel to generate a main PSM list with main ranking score by computing the main ranking score from the ranking score of each respective PSM of each respective search; selecting highest ranking PSMs according to respective main ranking scores (Claim 1), when a difference in probability scores is below a defined percentage of the average probability score, the lower-ranked PSM are obtained and added to the modified sequence dataset (claim 9, the percentage is a routine parameter choice)
With respect to the limitations of Claims 1, Cobbold et al. teaches they identified 95 phosphopeptides presented on the surface of primary hematological tumors and normal tissues, including 61 that were tumor-specific. Phosphopeptides were more prevalent on more aggressive and malignant samples. They identify sequences of interest and store them (abstract, storing in a modified sequence dataset, a plurality of modified sequences each including the PTM and sequences corresponding to the selected highest ranking PSMs, wherein the modified sequence dataset stores an indication of binding motifs defined by a plurality of identified PTM and corresponding sequence; and (Claim 1) Cobbold et al also teaches small-molecule therapies targeting these pathways have met with considerable clinical success, providing a powerful argument for immunotherapies that target similar deregulated pathways. Protein phosphorylation is the dominant mechanism involved in oncogenic signaling processes. They previously showed that phosphorylation is preserved on peptides during antigen processing for presentation by both major histocompatibility complex (MHC) class I and II molecules. This suggests that phosphopeptide antigens derived from cancer-related phosphoproteins could serve as immunological signatures of “transformed self” Moreover, phosphorylation can enhance the binding of peptides to MHC class I molecules, creating “neoantigens.” Collectively, these results suggest that phosphopeptides are attractive targets for cancer immunotherapy (providing the modified sequence dataset for selecting a certain binding motif having a certain PTM and corresponding amino acid sequence from the modified sequence dataset capable of specifically binding an MHC presented peptide for treatment of the target disease (Claim 1)
Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al teaches a mass spectrometry methods to identify antigens of interest. All parts previously existed in the prior art and a person of ordinary skill would recognize how to put them together as Bassani-Sternberg et al. teaches using mass spectrometry to gather modified peptide data associated with a disease. Cox et al. teaches a key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Kong et al. teaches MSFragger which automatically creates modifications to peptides in order to identify potential fragments. Bjornson et al. teaches the running the search in parallel to improve computational efficiency which would be an obvious variation for any person of ordinary skill in the art trying to improve computation. Shteynberg et al. teaches aggregation of different methods to identify PTMs and ranking to find the most likely options. Cobbold et al teaches identifying important MHC presenting peptides and then designing custom immunotherapies based on the discovered peptides. There is a reasonable expectation of success because each part works individually and applicant is just putting together separate working parts. Therefore, it is expected to work together giving it a reasonable chance of success.
Claim 2, 18, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al as applied to claims 1, 3, 8, 9, 10, 11, 12, 14, 15, 16, 17, above in further view of Jurtz et al. (Jurtz, V.; Paul, S.; Andreatta, M.; Marcatili, P.; Peters, B.; Nielsen, M. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. The Journal of Immunology 2017, 199 (9), 3360–3368.) in further view of Solleder et al. (Solleder, M.; Guillaume, P.; Racle, J.; Michaux, J.; Pak, H.-S.; Müller, M.; Coukos, G.; Bassani-Sternberg, M.; Gfeller, D. Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands. Molecular & Cellular Proteomics 2020, 19 (2), 390–404). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 3, 8, 9, 10, 11, 12, 14, 15, 16, 17 have been taught by Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al. above.
Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al does not teach
further comprising: creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset (Claim 2)
each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, and training a machine learning (ML) model using the training dataset (Claim 2)
each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; (Claim 2)
and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model (Claim 2)
and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (Claim 2)
comprising: creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset, each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, the modified sequence dataset created as in claim 1,each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model, and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (Claim 18),
comprising receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs; feeding the input into an ML model created as in claim 1; and obtaining as an outcome of the ML model, for the input of (i) an indication of whether the certain modified sequence is predicted to fit a motif that binds to a cell of the MHC type, and for the input of (ii) obtaining at least one motif predicted to be created from the full protein length and PTMs. (Claim 19)
With respect to the limitations of Claims 2, 18, 19, Jurtz et al. teaches NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data for increased accuracy. It uses the sequence. They trained the NetMHCpan method version 4.0 for the prediction of the interaction of peptides with MHC class I (an MHC type) molecules integrating BA and MS EL data and (abstract, pg. 3362, col. 1, paragraph 6) and A neural network ensemble was trained, as described by Nielsen and Andreatta (1), using 5-fold nested cross-validation. Networks with 60 and 70 hidden neurons were trained, leading to an ensemble of 40 networks in total. The inputs to the neural networks consisted of the peptide and the MHC molecule in terms of a pseudo-sequence (pg. 3361, col. 2, paragraph 6, further comprising: creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset (Claim 2) each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; (Claim 2) and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model (Claim 2) and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (Claim 2) comprising: creating a training dataset by labelling each modified sequence for each respective motif of the modified sequence dataset, each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, the modified sequence dataset created as in claim 1,each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model, and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (Claim 18), comprising receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs; feeding the input into an ML model created as in claim 1; and obtaining as an outcome of the ML model, for the input of (i) an indication of whether the certain modified sequence is predicted to fit a motif that binds to a cell of the MHC type, and for the input of (ii) obtaining at least one motif predicted to be created from the full protein length and PTMs. (Claim 19)
With respect to the limitations of Claims 2, 18, 19, Solleder et al. teaches a training set containing an amino acid sequence, covering 72 HLA-I alleles, containing the sequence and the phosphorylation site. Solleder et al. teaches prediction of the presentation of phosphorylated peptides on HLA-I molecules has been poorly explored, mainly because of the lack of training data. Here, we curated phosphorylated HLA-I ligands across many immunopeptidomics studies to investigate molecular properties of interactions between phosphorylated peptides and HLA-I molecules and developed the first predictor for HLA-I interactions with phosphorylated peptides. A total number of 2,066 unique phosphorylated peptide sequences were retrieved, representing 2,585 unique HLA-I-phosphorylated peptide interactions with 72 different HLA-I alleles and different phosphorylation sites on serine, threonine and tyrosine (different type of PTM modifications) as variable modifications where it could be phosphorylated. The ability to integrate these different features into a robust predictor of phosphorylated HLA-I ligands explains the improvement over existing tools and provides a rationale for training on both unmodified and phosphorylated HLA-I ligands. All HLA-C but also most HLA-B alleles in our data set, including those with low fractions of phosphorylated ligands, contain arginine at position 62 (Discussion, each modified sequence including an amino acid sequence, PTM type, and position of the PTM on the amino acid sequence, and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model (Claim 2) each label including an indication of one or more of: an MHC type, parent gene, and position of the motif within a full protein length; and training a machine learning (ML) model using the training dataset, wherein for an input of a certain modified sequence defined by a combination of an amino acid sequence and at least one PTM into the ML model, an indication of whether the certain modified sequence is predicted to fit a binding motif that binds to a cell of the MHC type is obtained as an outcome of the ML model, and for an input of an amino acid sequence of a full protein length and PTMs into the ML model, at least one modified sequence predicted to fit a binding motif is obtained as an outcome of the ML model. (Claim 18), comprising receiving an input of one of: (i) a certain modified sequence defined by an amino acid sequence and a PTM, and (ii) an amino acid sequence of a full protein length and PTMs; feeding the input into an ML model created as in claim 1; and obtaining as an outcome of the ML model, for the input of (i) an indication of whether the certain modified sequence is predicted to fit a motif that binds to a cell of the MHC type, and for the input of (ii) obtaining at least one motif predicted to be created from the full protein length and PTMs. (Claim 19)
Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al teaches a mass spectrometry method to identify antigens of interest it would be obvious to add machine learning taught by Jurtz et al. in view of Solleder et al. to improve the method. Jurtz et al. teaches the finding of peptides with a strong binding affinity using machine learning and Solleder et al. teaches the use of specific phosphorylation sites as well as PTM types in order to improve prediction. It would be obvious to add the information of Solleder et al. in order to improve the machine learning model taught by Jurtz et al. There is a reasonable expectation of success because each part works individually and applicant is just putting together separate working parts. Therefore, it is expected to work together giving it a reasonable chance of success.
Claim 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al as applied to claims 1, 3, 8, 9, 10, 11, 12, 14, 15, 16, 17, above in further view of Duncan et al. (Duncan, D. T.; Craig, R.; Link, A. J. Parallel Tandem: A Program for Parallel Processing of Tandem Mass Spectra Using PVM or MPI and X!Tandem. Journal of Proteome Research 2005, 4 (5), 1842–1847.) The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 3, 8, 9, 10, 11, 12, 14, 15, 16, 17 have been taught by Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al. above.
With respect to the limitations of Claims 5, 6, Cox et al. teaches a key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra (abstract) False discovery rates for the same initial probability score can still depend on the number of modifications and on the mass of the peptide (pg. 1803), Cox et al. also teaches oxidation of methionine and N-terminal protein acetylation were used as variable modifications for all searches. A mass tolerance of 6 ppm was used for the peptide mass. (pg. 1795, col. 2, paragraph 4) It also uses score for ranking the peptides in MaxQuant searches from the beginning and it also determines the localization probability of modifications in peptides, MSFragger begins by performing an in silico digestion of the pro tein database (Fig. 1a). It then removes redundant peptides and orders them by their theoretical mass (including any modified peptides generated as a result of variable modifications), wherein statistical parameters used in a subsequent false discovery rate (FDR) calculation are distorted by a plurality of searches of a same reference dataset over different software instances executed by the plurality of processors, and wherein merging further comprises: removing duplicated PSM from the PSM aggregation dataset by using unmodified hits combined histogram to evaluate a number of duplicated PSM and identify the duplicated PSM for removal thereof, and recalculating an expectation based on a restored score histogram for each PSM. (Claim 5) further comprising: computing a plurality of quality assignment measures, and performing the following using the quality assignment measures: validating the PTM of each member of the PSM aggregation dataset according to the quality measures; filtering ambiguous assignments and isobaric decoys of the PSM aggregation dataset according to a filtering threshold; ranking members of the PSM aggregation dataset; and selecting the highest ranking PSMs according to the highest ranked member of the PSM aggregation dataset (Claim 6)
With respect to the limitations of Claims 4, 5, 7, Shteynberg et al. teaches iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. FDR is a way to rank the accuracy of identification which inherently tells the most reliable proteins. A person of ordinary skill in the art would know to take the lowest FDR scores as the most reliable matches. (abstract) and PeptideProphet calculates the posterior probability of a correct PSM, individually for each search engine output. iProphet combines multiple lines of evidence and computes accurate probabilities at the level of unique peptide sequences, assisted by the introduction of new grouping variables, Using a probability to select the best ones and remove low ones is an obvious modification of a person of ordinary skill in the art (pg. 7 final paragraph, wherein the highest ranking PSMs are selected from the PSM aggregation dataset (Claim 4), wherein statistical parameters used in a subsequent false discovery rate (FDR) calculation are distorted by a plurality of searches of a same reference dataset over different software instances executed by the plurality of processors, and wherein merging further comprises: removing duplicated PSM from the PSM aggregation dataset by using unmodified hits combined histogram to evaluate a number of duplicated PSM and identify the duplicated PSM for removal thereof, and recalculating an expectation based on a restored score histogram for each PSM (Claim 5), this is an obvious variation of IProphet of removing duplicates and to recalculate the score with duplicates removed. It is the principal of using a parallel search approach, further comprising: computing a probability score indicative of match accuracy for each PSM, wherein the highest ranking PSMs are selected according to highest probability (Claim 7))
Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al does not teach
allocating a respective subset of the plurality of combinations to a plurality of processors connected for parallel processing, each respective processors searching the respective spectra element on the respective subset to identify a respective set of PSM, merging the respective set of PSM of each respective processor to create a PSM aggregation dataset, (Claim 4)
With respect to the limitations of Claims 4, Duncan et al teaches the first mapper generates all possible sequences and modified sequences defined in the search parameters for a given fasta database (Figure 1 caption). Hydra enumerates the candidate peptides and their modified form, groups them and then scores them on separate machines. (allocating a respective subset of the plurality of combinations to a plurality of processors connected for parallel processing, each respective processors searching the respective spectra element on the respective subset to identify a respective set of PSM, merging the respective set of PSM of each respective processor to create a PSM aggregation dataset, (Claim 4)
Kong et al. in view of Bassani-Sternberg et al. in view of Cox et al. in view of Bjornson et al. in view of Shteynberg et al. in view of Cobbold et al teaches a mass spectrometry and machine learning methods to identify antigens of interest it would be obvious to add parallel processing as taught by Duncan et al as it adds allocating Hydra enumerates the candidate peptides and their modified form, groups them and then scores them on separate machines. There is a reasonable expectation of success because all Duncan et al. adds computing in parallel of the same data in order to speed it up. This is a routine optimization any person of ordinary skill in the art would do to improve the results.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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