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Data Processing Algorithms for Analysis of High Resolution MSMS Spectra of Peptides with Complex Patterns of Posttranslational Modifications Shenheng Guan and Alma L. Burlingame Problem Input: An MS/MS spectrum of a mixture of peptides: Heavily modified protein Same amino acid sequence Same PTM Same total number of PTMs Different PTM configurations Example Two peptides with two methylations each. LATK[+32]AARKSAE LATK[+16]AARK[+16]SAE Problem: Identify the PTM configurations Estimate their relative abundance Work flow Peptide identification Input A deisotoped MS/MS spectrum of a mixture of peptides An identified peptide, the type of PTMs and the number of PTMs. Example Peptide: LATKAARKSAPATGGVKKPHRYRPGTVALRE PTM: Methylation #PTM: 4 Problem Identify the PTM configurations Estimate their relative abundance All possible configuration Assumption: All methylations are on lysine residues Each lysine residue has at most 3 methyl groups. Configuration identification Score of Spectrum-Configuration-Pair Spectrum S: ETD peak list Configuration C: theoretical peak list (c-ion) Sc(S,C) is the number of matched peaks in the real peak list and the theoretical peak list. Greedy algorithm Compute the matching score for each configuration Remove the configure with the highest score from the configuration set and remove the peaks in S that are matched to the configuration Repeat the above steps until all configurations have score 0 Configuration identification results Estimation of relative abundance We have four identified configurations C1,C2,C3,C4. x1, x2, x3, x4 the relative abundance Consider the ith c-ion with charge z Sum equals to 1 Five possible peaks p0, …, p4 Suppose p2 is matched to C1, C2 Observed peak intensity I(p2) Theoretical peak intensity ( x1 x2 ) I(p 0 j 4 j ) Compute the observed and theoretical peak intensity pair for each matched c-ion Estimation of relative abundance Find x1, x2, x3, x4 such that the sum of the squared errors of these intensity pairs is minimized. Standard non-negative least-square procedure A Novel Approach for Untargeted Posttranslational Modification Identification Using Integer Linear Optimization and Tandem Mass Spectrometry Richard C. Baliban, Peter A. DiMaggio, Mariana D. Plazas-Mayorca, Nicolas L. Young, Benjamin A. Garcia and Christodoulos A. Floudas Bottom up PTM identification Two approaches Tags Non-tags Restricted Unrestricted PILOT_PTM Preprocessing Remove all peaks related the precursor ion Only keep locally significant peaks Deisotope Remove neutral offset if the peak doe not have a complementary peak. Each candidate peak has a list of supporting peaks. ILP Model Input Theoretical peak bk A preprocessed deisotoped spectrum S={ a1,a2,…,am } A peptide (theoretical b-ion peak list) P={ b1b2…bn} A list of all known PTMs CSk is the set of all possible peaks (indices) in S that bk can be matched to with PTMs Real peak aj Posj is the set of all possible peaks (indices) in P that aj can be matched to with PTMs Supportj is the set of all peaks (indices) supporting peak j in S Multj is the set of all peaks (indices) peak j supports ILP Model Binary variable pj,k = 1 if peak aj in S is matched to bk in P, otherwise pj,k = 0 yj = 1 is peak aj is a supporting peak or matched peak, otherwise yj = 0 ILP Model Objective Subject to One peak in P can only match one peak in S One peak in S can only match one peak in P ILP Model Subject to: No three consecutive missing peaks The intensity of peak i is counted iff the exists one peak j such that peak i supports j and peak j is a matched peak. ILP Model Solve using CPLEX Report top-10 variable assignments Existing problem No constraints that require the distance between two neighboring matched peaks should match the mass of a residue (with PTM) New constraints For each pj,k Set of candidate ion peaks j’ with respect to k’ such that no valid jump exists between j and j’ The maximum and minimum masses that can be reached from j, respectively New constraints Neighboring matched peaks do not conflict Conflicting matched peaks must have a matched peak between them The distance between two matched peaks should be bounded Postprocessing Re-scoring 10 candidate modified candidate peptides Cross-correlation score Recheck modifications if there are unmatched peaks indicating nonmodification Test data sets Test set A: 44 CID spectra (Ion trap), 174 ETD spectra (Orbitrap) of chemically synthesized phosphopeptides, manually validated Test set B: 58 ECD spectra (FTICR) of Histone H3-(1–50) N-terminal Tail, manually validated Test set C: 553 CID spectra (Orbitrap) of Propionylated Histone Fragments, manually validated Test set D: 525 modified and 6025 unmodified CID spectra (Orbitrap) from chromatin fraction. Identified by SEQUEST and validated by MASCOT and remove low quality spectra manually Test set E: unmodified 36 (Ion trap), 37 (Q-TOF), 4061(Orbitrap) CID unmodified spectra. Validated as test set D Residue predication accuracy Peptide prediction accuracy Comparison on test sets C and D1 Peptide and residue prediction accuracy Comparison on test sets C and D1 Subsequence prediction accuracy Running time Q&A