PepHMM: A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search Laxman Yetukuri T-61.6070: Modeling of Proteomics Data Outline  Motivation  Basics: MS and MS/MS for Protein Identification  Computational Framework of Database Search  Scoring Algorithms  PepHMM  MOWSE  Results  Summary Motivation  Proteomics studies- dynamic and context sensitive  Speed and accuracy of omics-driven methods  High throughput MS-based approaches  Real analysis starts with protein identification  Protein identification is challenging  The heart of protein identification algorithm is scoring function Protein Identification Is Challenging  Sample Contamination  Imperfect Fragmentation  Post translational Modifications  Low signal to noise ratio  Machine errors Basics: MS and MS/MS for protein Identification Liquid Chromatography Trypsin Digest Mass Spectrometry Precursor selection + collision induced dissociation (CID) MS/MS Computational Problem Nesvizhskii and Aebersold, Drug Discovery Today, 2004, 9, 173-181 Peptide Fragmentation: b & y ions yn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NHCH-R’ i+1 Ri bi R” i+1 bi+1 Peptide Fragmentation: b & y ions … 88 145 292 405 534 663 778 907 1020 1166 b ions S 1166 G 1080 F 1022 L 875 E 762 E 633 D 504 E 389 L 260 K 147 y ions y6 100 % Intensity y7 y5 b3 y2 y3 b4 y4 b5 b6 b7 b8 y b9 8 y9 0 250 500 750 1000 m/z Peptide Fragmentation with other ions xn-i y n-i z n-i yn-i-1 -HN-CH-CO-NH-CH-CO-NHCH-R’ i+1 Ri ai bi ci R” i+1 bi+1 Peptide Identification Two main methods for tandem MS:  De novo interpretation  Sequence database search De Novo Interpretation % Intensity 100 L SGF KL E E E E D E L D E F L 0 250 500 750 G 1000 m/z Sequence Database Search  Widely used approach  Compares peptides from a protein sequence database with experimental spectra  Scoring function summarise the comparison   Critical for any search engine Score each peptide against spectrum  Cross correlation (SEQUEST)  MOWSE scoring and its extensions (MASCOT)  Probabilistic scoring systems (OMSSA, OLAV, ProbID…..) PepHMM is HMM based probabilistic scoring function Computational Framework for pepHMM  MSDB based peptide extraction  Hypothetical spectrum generation   b,y,y-H2O,b-H2O,b2+ and y2+ Computing probabilistic scores  Initial classification :Match, missing or noise  Compute pepHMM scores (discussed later)  Compute Z-score  Compute E-score Contents of pepHMM Model  PepHMM combines the information on correlation among the ions, peak intensity and match tolerance  Input – sets of matches, missing and noise  Model is based on b and y ions  Each match is associated with observation (T,I)  Observation state = observed (T,I)  Hidden state =True assignement of the observations Model Structure Four possible assignments corresponding to four hidden states Model Computation Goal: Calculate highest score peptide in the database max Pr( s p, ) . pD   ( a ,  ,  , b ,  y ) Let a path in HMM be    1 2 .... n represents configuration of states, probability of the path Pr( , s p, )  Pr( , M  ) n 1   (a i 0 i , i 1 e i 1  )* # noise Model Computation… Considering all possible paths Pr( s p, )   Pr( , M  ).  Forward algorithm: Probability of all possible Paths from the first position to state v at postion i (i ) fv     Pr( 1 .....,  i 1  u , M  ). 1 .... i 1 (i ) fv ( i 1)  ev ( f u u (i ) ( i 1) au ,v ), Pr( M  )   f v( n ) v Emmission Probabilities Probability of observing (Tb,Ib) and (Ty, Iy) for the state 1 at position i e1(i )  Pr((Tb , I b ), (Ty , I y )  )  Pr((Tb , I b  ) Pr((Ty , I y )  )  Pr(Tb  ) Pr( I b  ) Pr(Ty  ) Pr( I y  ), Pr( Tb  Pr( ), Pr( Ib  ), Pr( Ty  Iy  ) ---Normal distribution ) ---Exponential distribution MOWSE Scoring System MOWSE Algorithm is implemented in MASCOT software Score  50,000 M P r ot   m i , n j Where f mi , j  i, j f i , j max incolumnj mi,j -elements of MOWSE frequence matrix Data Sets ISB data set: 1. A,B mixtures of 18 different proteins with modifications/relative amounts 2. Analysed using SEQUEST and other in-house Software 3. Data set is curated 4. Final data set with charge 2+ for trypsin digestion contains 857 spectra 5. 5-fold cross validation by random selection -Training set :687 spectra -Testing set : 170 spectra 6. EM algorithm is used for estimating parameters Results: Distributions of Ions b and y ions Match Tolerance Noise Parameter estimates  , ,  Comparative Studies Dat set selection repeated 10 times to select both training and test data set For each group parameters are similar values Prediction is considered correct if the peptide has highest score Independent Data Set A.Y’s Lab: The other independent data set for comparing with other tools like SEQUEST and MASCOT size of data set =20,980 spectra False/True Positive Rates Summary  Developed probabilistic scoring function called pepHMM for improving protein identifications  PepHMM outperform other tools like MASCOT with low false postive rate (always?)  Can this handle other type of ions other than b and y ions  Need to handle post translational modifications