Protein Identification from Tandem Mass Spectra with Probabilistic Language Modeling

1. Protein Identification from Tandem Mass Spectra with Probabilistic Language Modeling Yiming Yang1,2, Abhay Harpale1 and Subramanian Ganaphathy1 1: Language Technologies Institute 2: Machine Learning Department School of Computer Science, Carnegie Mellon University

2. Outline • Motivation & Background • Two probabilistic approaches • Experiments @Yiming Yang, ECML 2009, Sept 8

3. Motivation • Proteins are important bio-markers for diseases, drug toxicity, therapeutic outcomes, etc. • Statistical approaches have been developed for protein identification in computational proteomics • Interdisciplinary research for comparing current solutions with successful methods in IR (information retrieval) for similar problems has been rare. • We address this research gap by • Analyzing a major limitation of popular approaches in protein ID • Proposing a new solution (Language Modeling for IR) @Yiming Yang, ECML 2009, Sept 8

4. The Protein ID Problem • Tandem mass (MS-MS) spectra are producedusing some chemical process on an input sample (e.g., blood) • A sample typically consists of multiple proteins. • The process segments each protein into many (hundreds) pieces, called peptides. • Peptides are further decomposed into ionized segments. • The MS-MS spectrum of a peptide is a series of spikes. • Each spike is the mass/charge (m/z) ratio of an ionized segment in the peptide. @Yiming Yang, ECML 2009, Sept 8

5. The Protein ID Problem (cont’d) • Protein identification requires a mapping from empirical (MS-MS) spectra to protein sequences in an DB • There are many protein sequence databases • SwissProt, for example, contains 280,000+ sequences • Each protein is defined as a sequence of amino-acid letters • Peptides in each protein are specified using cleaving rules • Each peptide has an amino-acid sequence and a corresponding theoretical (“expected”) spectrum @Yiming Yang, ECML 2009, Sept 8

6. Theoretical Spectra of peptides in a DB Empirical Spectra of peptides in a sample Mapping Matching • Fourier Transformation • Probabilistic Models • Heuristic Rules @Yiming Yang, ECML 2009, Sept 8

7. Theoretical Spectra Empirical Spectra Mapping Matching Words in L1 Words in L2 Matched Words (in L2) Doc Retrieval Matched Documents (in L2) @Yiming Yang, ECML 2009, Sept 8

8. Outline • Motivation & Background • Two probabilistic approaches • Experiments @Yiming Yang, ECML 2009, Sept 8

9. A Popular Approach in Protein ID(ProteinProphet by Nesvizhskii et al., 2003) • Given the predicted peptides based on MS-MS spectra, the probability for each candidate protein is estimated as: -- estimates the probability for a Boolean OR logic -- typically produces many false positives @Yiming Yang, ECML 2009, Sept 8

10. A Popular Approach in IR • Language Models (Ponte 1998; Lafferty & Zhai, 2001; …) • Query (q) is represented using a bag of words • Document (d) is represented using a bag of words • KL-divergence of the two words distributions (θqandθd ) is Cross entropy H (θq ||θd) -- not affect doc ranking -- a “soft” measure for the Boolean AND logic @Yiming Yang, ECML 2009, Sept 8

11. LM for Protein ID • Query language model for predicted peptides • Document language model for each protein sequence @Yiming Yang, ECML 2009, Sept 8

12. Outline • Motivation & Background • Two probabilistic approaches • Experiments @Yiming Yang, ECML 2009, Sept 8

13. Data Sets • PPK (Purvine et al., 2003) • 2995 empirical spectra from a mixture of 35 proteins • 4535 protein sequences (325,812 unique peptides) • Mark12 • 9380 empirical spectra from a mixture of 12 proteins • 50,012 protein sequences (5,149,302 unique peptides) randomly sampled from the SwithProt database • Sigma49 • 12,498 empirical spectra from a mixture of 49 proteins • 50,049 protein sequences (2,571,642 unique peptides) randomly sampled from the SwithProt database @Yiming Yang, ECML 2009, Sept 8

14. Systems • Prob-AND • Our proposed method • Prob-OR • Nesvizhskii’s method, our own implementation • Conventional Vector Space Model (TFIDF-cosine) • Supported by the Lemur search engine (Callan, 2002) • X!Tandem • A popular software (online available) for protein/peptide ID • All the system, except X!Tandem, used SEQUEST to predict a set of peptides (as the “query”). • Each system produces a ranked list of proteins per query. @Yiming Yang, ECML 2009, Sept 8

15. Metrics • Mean Average Precision (MAP) • Standard metric in IR for evaluating ranked lists • Evaluate each ranked list from the top to each position where a true positive document is retrieved • Recall = TP/(TP + FN) • Precision = TP/(TP + FP) • TP = # of true positives, TN = # of true negatives • FP = # of false positives, FN = # of false negatives • Average the precision scores in recall intervals among 0%, 10%, 20%, …, 100% (“11-pt AVGP”) • Compute the mean of AVGP across all intervals and for all queries @Yiming Yang, ECML 2009, Sept 8

16. Main Results @Yiming Yang, ECML 2009, Sept 8

17. Statistical Significance Tests on Proportions @Yiming Yang, ECML 2009, Sept 8

18. Summary • The first interdisciplinary investigation/evaluation of state-of-the-art IR methods (LM and VSM) in protein identification • Prob-AND (LM) is a better choice of criterion than prob-OR in combining peptide-level evidence, improving precision significantly in the high-recall regions. • Understanding the nature of proteomic data/problems by researchers with different backgrounds (IR or ML) is hard, but, the outcome is and will be rewarding. @Yiming Yang, ECML 2009, Sept 8

19. Future Research • Finding the “best” protein mixture (Arnold et al., PSB 2007) • Instead of predicting each protein independently • Reduces to solving the minimum set cover problem (NP-hard) • Revised as to find the most likely protein mixture (Li et al., 2008) • Greedy approximation strategies • Using Gibbs sampling (local maxima, efficiency issues) • Better results than ProteinProphet (prob-OR) on Sigma49 • Comparative evaluation (with LM, VSM, etc.) would be informative • Scalability for high-recall predictions from very large protein databases? @Yiming Yang, ECML 2009, Sept 8

20. Thanks! @Yiming Yang, ECML 2009, Sept 8