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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-12, and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. ("Exhaustive Cross-linking Search with Protein Feedback", as cited in the IDS).
Regarding claim 1, Chen et al. teaches a system for detecting protein-protein interaction and probing protein structures (see Introduction/pg. 1, disclosing that cross-linking mass spectrometry (XL-MS) is used to deduce protein-protein interactions (PPIs) and decipher protein structures in a high-throughput manner.), comprising: a receptacle for receiving a cross-linked precursor peptide produced by digestion of a protein cross-linked with a cleavable or a non-cleavable crosslinker (see Introduction/pg. 1, Fig. 1/pg. 3, disclosing that teach protein in the database is first digested into peptides, and only use the peptides containing a link site(s) to construct a vector for that protein, where cross-linkers can be divided into non-cleavable cross-linkers and cleavable cross-linkers.); a mass spectrometer for producing precursor ions and generating MS1 spectrum so as to determine the mass spectrum of the ions with their charge states and masses; and processing at least one precursor ion to undergo a fragmentation treatment to generate an interested MS2 spectrum (see Introduction/pg. 1, disclosing using cross-linking mass spectrometry, cleavable cross-linker breaks into two residues in the collision-induced dissociation (CID) process and generates signature ions in the MS/MS (MS2) spectrum, which can help directly pinpoint the α/β peptide mass.); a precursor mass refinement module for analyzing an elution profile generated by the mass spectrometer and extracting and merging MS1 spectra obtained before and after the interested MS2 spectrum in the elution profile to form a complete isotope cluster of the precursor ion (see Precursor mass refinement/pg. 12, disclosing that precursor mass refinement is prevalent in XL-MS software. The nearby MS1 spectra of the current MS2 spectrum is extracted and merged to form a new spectrum. Four preceding MS1 and three subsequent MS1 were used to implement this step and restrict the scan event. See also Fig. S7/pg. 13, disclosing and displaying nearby MS1 spectra are merged to generate a complete isotope cluster, and showing current spectrum MS2 and elution profile of MS spectras.); a MS2 spectrum scoring module for matching the interested MS2 spectrum with the complete isotope cluster utilizing a scoring function S(x) and detecting and storing top p hits of the MS2 spectrum and their scores, wherein the MS2 spectrum scoring module further retrieves the top α peptide and β peptide hit of the MS2 spectrum as significant peptides and generates a scoring histogram (see Fig. 1/pg. 3, disclosing and displaying a protein feedback pipeline. The input MS2 spectra are first matched with peptide sequenced by the scoring function S(x). The algorithm stores the top p hits for each spectrum during the matching process. The first α and β hits of all the spectra and plot the histogram of the score distribution in a further step.); a protein score database constructed based on the scoring histogram and significant peptides for providing global information to connect peptides to proteins, comprising protein sequences in a biological system (see Protein feedback pipeline/ pg. 3-4, disclosing after scoring peptides, the algorithm retrieves the top α and β hit of each spectrum and draws the histogram of their scores in the second step. A well-designed scoring function should have a property that’s higher than the scores, the more confident the peptide. All the significant peptides are then used to build a protein score database. Different from peptide databases that are used to provide potential peptide sequences to identify experimental spectra, the protein score database is used to provide global information to connect peptides to proteins and is established by the theoretical protein sequence and the scoring function.); a feedback mechanism processor for retrieving the stored top p hits in the protein score database and identifying at least one peptide candidate in the protein score database, wherein a peptide candidate having the largest protein score among the at least one peptide candidate is identified as a matching result for the interested MS2 spectrum; identifying the peptide interaction or protein structure from the matching result (see Protein feedback pipeline/pg. 4, disclosing The last step is the feedback procedure. The algorithm returns to the stored top hits and looks up the peptide candidates in the protein score database. Among the peptide candidates, we regard the one having the largest protein score as the peptide match for that spectrum.); and a cross-linking dataset, comprising cleavable cross-linker data or non-cleavable cross-linker data, both obtained from proteins in a biological system cross-linked by a cleavable or non-cleavable crosslinking reagent (see Evaluation using real dataset/pg. 6, disclosing exhaustive cross-link search method with protein feedback (ECL-PF) having its performance evaluated using real datasets of cross-linkers DSSO and DSBU to DSBSO.).
Regarding claim 2, Chen et al. teaches the system of claim 1, further comprising a local alignment module configured to provide a series of compensation matches while there is no signature ion available on the interested cleavable crosslinking MS2 spectrum (see Exhaustive cross-linking search/pg. 2, disclosing a local alignment module is designed for compensation when no signature ion is available in the current MS2 spectrum.).
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Regarding claim 3, Chen et al. teaches the system of claim 1, wherein the precursor mass refinement module selects and calibrates the complete isotope cluster with a theoretical isotope cluster to find a monoisotopic mass using a Pearson correlation coefficient with a formula of:
; wherein x and y stand for the theoretical isotope cluster and the complete isotope cluster, respectively (see Precursor mass refinement/pg. 12, Equation S5, disclosing that after obtaining the new spectrum, we compare the theoretical isotope cluster with each possible position in the new MS1 and calculate the corresponding Pearson correlation coefficient using Eq. S5.:
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633
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where x and y stand for the theoretical and experimental isotope clusters, respectively.).
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Regarding claim 4, Chen et al. teaches the system of claim 1, wherein the scoring is conducted with the following counting formula:
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; wherein k is the link site(s) number in an individual protein and ⃗P is a protein vector, and the protein score PS is calculated by summing the counting result (see Protein feedback pipeline/pg. 4, Eq. 4, disclosing different from peptide databases that are used to provide potential peptide sequences to identify experimental spectra, the protein score database is used to provide global information to connect peptides to proteins and is established by the theoretical protein sequence and the scoring function. The scoring is conducted with the following formula:
, where k is the link site(s) number in the individual protein and ⃗P is the protein vector. The protein score PS is calculated by summing the counting result.).
Regarding claim 5, Chen et al. teaches the system of claim 1, wherein the proteins cross-linked by the cleavable or non-cleavable crosslinking reagent are further filtered, enriched, and digested to produce cross-linked peptides (see Introduction/pg. 1, Fig. 1/pg. 3, disclosing that teach protein in the database is first digested into peptides, and only use the peptides containing a link site(s) to construct a vector for that protein, where cross-linkers can be divided into non-cleavable cross-linkers and cleavable cross-linkers.).
Regarding claim 7, Chen et al. teaches the system of claim 2, wherein the precursor mass refinement module has a default correlation cutoff value set as 0.9 to determine the complete isotope cluster (see Precursor mass refinement/pg. 12, disclosing that the best fit is the new calculated precursor mass result calculated using the corresponding Pearson correlation coefficient from Eq. S5. In ECL-PF, if it can't observe any coefficient larger than the defined threshold (0.9 by default), then it retains the originally provided precursor mass from the RAW file.).
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Regarding claim 8, Chen et al. teaches the system of claim 1, wherein the feedback mechanism processor utilizes a XlinkX scoring function as the default for the cleavable cross-linking data:
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; wherein
x
=
4
111
M
p
r
e
c
*
ϵ
; ϵ is the MS2 tolerance (ppm); and f is equal to
α
α
+
β
f
t
o
t
a
l
when β peptide score is calculated, where α and β denote α and β peptide mass and ftotal is the total number of fragmented ions in the MS2 spectrum (see Protein feedback pipeline/pg. 2, Eq. 1, disclosing that In ECL-PF, three major alternative scoring functions are provided for users to choose, one of which is a simplified XlinkX scoring function, as seen in Eq. 1:
, where ϵ is MS2 tolerance (ppm) and f is equal to α/(α+β)×ftotal when we calculate α peptide score or is equal to β/(α+β)×ftotal when we calculate β peptide score, where α and β denote α and β peptide mass and ftotal is the total number of fragmented ions in the MS2 spectrum.).
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Regarding claim 9, Chen et al. teaches the system of claim 1, wherein the feedback mechanism processor utilizes a Xcorr scoring function as the default for the non-cleavable cross-linking data:
; wherein ⃗e is the vectorization form of the input MS2 spectrum and ⃗t is the vectorized theoretical peptide sequence generated from fragmented peptide N terminal ions (b ions) and C terminal ions (y ions) (see Protein feedback pipeline/pg. 3, Eq. 3, disclosing the scoring function stores the top p peptide candidates and their corresponding scores for further judgment. In ECL-PF, we provide three major alternative scoring functions for users to choose,
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including an Xcorr scoring function (SEQUEST) as seen in Eq. 3:
, where ⃗e is the vectorization form of the experimental MS2 spectrum and ⃗t is the vectorized theoretical peptide sequence generated from the b/y ions.).
Regarding claim 10, Chen teaches a method of detecting protein-protein interaction and probing protein structures utilizing the system of claim 1 (see Introduction/pg. 1, disclosing that cross-linking mass spectrometry (XL-MS) is used to deduce protein-protein interactions (PPIs) and decipher protein structures in a high-throughput manner.), comprising: obtaining a cross-linked precursor peptide produced by digestion of a protein cross-linked with a cleavable or a non-cleavable crosslinker as a sample (see Introduction/pg. 1, Fig. 1/pg. 3, disclosing that teach protein in the database is first digested into peptides, and only use the peptides containing a link site(s) to construct a vector for that protein, where cross-linkers can be divided into non-cleavable cross-linkers and cleavable cross-linkers.); subjecting the sample to the mass spectrometry to produce precursor ions and generate MS1 spectra used to determine the charge states and masses of precursor ions (see Introduction/pg. 1, disclosing using cross-linking mass spectrometry, cleavable cross-linker breaks into two residues in the collision-induced dissociation (CID) process and generates signature ions in the MS/MS (MS2) spectrum, which can help directly pinpoint the α/β peptide mass.); processing at least one precursor ion of the precursor ions to undergo a fragmentation treatment to generate an interested MS2 spectrum (see Protein feedback pipeline/pg. 2, disclosing fragmented ions in the MS2 spectrum.); extracting and merging MS1 spectra obtained before and after the interested MS2 spectrum in an elution profile to form a complete isotope cluster of the precursor ion utilizing the precursor mass refinement module (see Precursor mass refinement/pg. 12, disclosing that precursor mass refinement is prevalent in XL-MS software. The nearby MS1 spectra of the current MS2 spectrum are extracted and merged to form a new spectrum. Four preceding MS1 and three subsequent MS1 were used to implement this step and restrict the scan event. See also Fig. S7/pg. 13, disclosing and displaying nearby MS1 spectra are merged to generate a complete isotope cluster, and showing current spectrum MS2 and elution profile of MS spectras.); inputting the MS2 spectrum to the MS2 spectrum scoring module for matching the interested MS2 spectrum with the complete isotope cluster (see Fig. 1/pg. 3, disclosing and displaying a protein feedback pipeline. The input MS2 spectra are first matched with peptide sequenced by the scoring function S(x). The algorithm stores the top p hits for each spectrum during the matching process. The first α and β hits of all the spectra are retrieved and plot the histogram of the score distribution in a further step.); detecting and storing top p of α peptide and β peptide hits of the MS2 spectrum and their scores, wherein the MS2 spectrum scoring module further retrieves the top p of α and β hits of the MS2 spectrum as significant peptides and generates a scoring histogram (see Fig. 1/pg. 3, disclosing and displaying a protein feedback pipeline. The input MS2 spectra are first matched with peptide sequenced by the scoring function S(x). The algorithm stores the top p hits for each spectrum during the matching process. The first α and β hits of all the spectra are retrieved and plot the histogram of the score distribution in a further step.); constructing the protein score database using the significant peptides and the scoring histogram retrieving the stored top p of α and β hits in the protein score database so as to identify at least one peptide candidate denoted with sequence from the protein score database, wherein the peptide candidate having the largest protein score among the at least one peptide candidate is identified as a matching result for the input MS2 spectrum (see Protein feedback pipeline/pg. 3, disclosing After scoring peptides, the algorithm retrieves the top α and β hit of each spectrum and draws the histogram of their scores in the second step. A well-designed scoring function should have the property that the higher the scores, the more confident the peptides. All the significant peptides are then used to build a protein score database.); and re-matching the sequence by using the protein score and outputting a cross-linked peptide-spectrum match for identifying the peptide interaction or protein structure from the matching result (see Protein feedback pipeline/pg. 3, disclosing each protein in the database is first digested into peptides, and only use the peptides containing a link site(s) to construct a vector for that protein. Next, the counting function C(x) is used to compute the protein score. In the last step, we go back to the stored top p peptides and find the largest protein score among these candidates to finish the matching process and output the results.).
Regarding claim 11, Chen et al. teaches the method of claim 10, wherein the MS2 spectrum is de-charged if the MS2 spectrum matches with the cleavable cross-linker data (see Fig. 1/pg .3, disclosing the general workflow of ECL-PF. Peptide database generation is separated from the spectra matching. In the steps shown in the figures, prior to precursor refinement, and spectra matching, spectra decharge is performed.).
Regarding claim 12, Chen et al. teaches the method of claim 10, further comprising adopting a target decoy database to control the output quality (see General workflow of ECL-PF/pg. 4, disclosing that the ECL-PF workflow employs a target-decoy strategy to filter the significant results.).
Regarding claim 14, Chen et al. teaches the method of claim 10, wherein the sample is further filtered, enriched, and digested to produce cross-linked peptides (see Introduction/pg. 1, Fig. 1/pg. 3, disclosing that teach protein in the database is first digested into peptides, and only use the peptides containing a link site(s) to construct a vector for that protein, where cross-linkers can be divided into non-cleavable cross-linkers and cleavable cross-linkers.).
Regarding claim 15, Chen et al. teaches the method of claim 10, in the step of retrieving, further catching top 20 hits for further justification after matching (see Choice of top hits/pg. 8, Table S3, disclosing optimizing the top p hit choices and proportional restriction by using real data sets to run ECL-PF with different combinations of p and proportion. In Table S3, it displays three scoring functions, where Sxcorr has 20 Top hits at a proportion of 0.65.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. as applied to claim 1 above, and further in view of Huang et al. (US PG-Pub 20220274963 A1).
Regarding claim 6, Chen et al. fails to teach wherein the biological system comprises cells, tissues, blood, serum, sputum.
However, in the analogous art of mass spectrometry cleavable heterobifunctional photoactivated cross-linkers, Huang et al. teaches using mass spectrometry to facilitate structural analysis of intra-protein interactions in proteins and inter-protein interactions in protein complexes. It’s disclosed that cross-linking mass spectrometry (XL-MS) is a unique structural tool capable of studying Protein-Protein Interactions (PPIs) due to its ability to simultaneously capture and identify PPIs with interaction contacts from native cellular environment. Yeast 26S proteasome is a target biological sample in this art in the MS-cleavable heterobifunctional cross-linker, which is drawn from yeast cells (see Haung et al., Abstract, [0005], [0183]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cross-linking mass spectrometry of Chen et al. to incorporate using biological samples such as cells (as taught by Huang et al.), for the benefit of sourcing proteasomes from natural products for analysis.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. as applied to claim 12 above, and further in view of Elias et al. ("Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry").
Regarding claim 13, Chen et al. fails to teach wherein the target decoy database is constructed by reversing the protein sequences.
However, in the analogous art of "Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry", Elias et al. teaches using target-decoy to distinguish incorrect peptide identification in liquid chromatography and tandem mass spectrometry in surveys of proteomes. For implementing the target-decoy strategy, A composite database is created by first obtaining a ‘target’ protein sequence database appropriate to the protein mixture to be analyzed. Next, a ‘decoy’ database is created to preserve the general composition of the target database while minimizing the number of peptide sequences in common between the target and decoy. This is most simply done by reversing the target protein sequences, although decoy databases can also be created by stochastic means, such as Markov chain modeling (see Elias et al., Abstract, Introduction, pg. 207-208).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cross-linking mass spectrometry and target-decoy of Chen et al. to incorporate creating a target-decoy by reversing the target protein sequences (as taught by Elias et al.), for the benefit of being able to implement a search strategy that manages sequence search engines returning as 'unmatchable' spectra and incorrect peptide identification, through a simplistic method using target-decoy (see Elias et al., Abstract).
Conclusion
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/TRACY CHING-TIAN COLENA/ Examiner, Art Unit 1797
/JENNIFER WECKER/ Primary Examiner, Art Unit 1797
