Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies.

Database-searching programs generally identify only a fraction of the spectra acquired in a standard LC/MS/MS study of digested proteins. Subtle variations in database-searching algorithms for assigning peptides to MS/MS spectra have been known to provide different identification results. To leverage this variation, a probabilistic framework is developed for combining the results of multiple search engines. The scores for each search engine are first independently converted into peptide probabilities. These probabilities can then be readily combined across search engines using Bayesian rules and the expectation maximization learning algorithm. A significant gain in the number of peptides identified with high confidence with each additional search engine is demonstrated using several data sets of increasing complexity, from a control protein mixture to a human plasma sample, searched using SEQUEST, Mascot, and X! Tandem database-searching programs. The increased rate of peptide assignments also translates into a substantially larger number of protein identifications in LC/MS/MS studies compared to a typical analysis using a single database-search tool.

[1]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[2]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[3]  Gilbert S Omenn,et al.  An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis , 2005, Proteomics.

[4]  R. Aebersold,et al.  Differential stable isotope labeling of peptides for quantitation and de novo sequence derivation. , 2001, Rapid communications in mass spectrometry : RCM.

[5]  R. Beavis,et al.  A method for reducing the time required to match protein sequences with tandem mass spectra. , 2003, Rapid communications in mass spectrometry : RCM.

[6]  D. Naiman,et al.  Probability model for assessing proteins assembled from peptide sequences inferred from tandem mass spectrometry data. , 2007, Analytical chemistry.

[7]  Steven P Gygi,et al.  Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations , 2005, Nature Methods.

[8]  Alexey I Nesvizhskii,et al.  Initial Proteome Analysis of Model Microorganism Haemophilus influenzae Strain Rd KW20 , 2003, Journal of bacteriology.

[9]  R. Aebersold,et al.  A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.

[10]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[11]  A. Nesvizhskii,et al.  Experimental protein mixture for validating tandem mass spectral analysis. , 2002, Omics : a journal of integrative biology.

[12]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[13]  R. Aebersold,et al.  ProbIDtree: An automated software program capable of identifying multiple peptides from a single collision‐induced dissociation spectrum collected by a tandem mass spectrometer , 2005, Proteomics.

[14]  A. Masselot,et al.  OLAV: Towards high‐throughput tandem mass spectrometry data identification , 2003, Proteomics.

[15]  Tom S. Price,et al.  EBP, a Program for Protein Identification Using Multiple Tandem Mass Spectrometry Datasets*S , 2007, Molecular & Cellular Proteomics.

[16]  Zhongqi Zhang,et al.  Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. , 2005, Analytical chemistry.

[17]  K. Cios,et al.  Improved Validation of Peptide MS/MS Assignments Using Spectral Intensity Prediction*S , 2007, Molecular & Cellular Proteomics.

[18]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[19]  John Skilling,et al.  Data Analysis-A Bayesian Tutorial: Second Edition , 2006 .

[20]  Gapped BLAST and PSI-BLAST: A new , 1997 .

[21]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[22]  David L Tabb,et al.  DBDigger: reorganized proteomic database identification that improves flexibility and speed. , 2005, Analytical chemistry.

[23]  W. Bruce Croft.  Combining Approaches to Information Retrieval , 2002 .

[24]  J. Yates,et al.  A hypergeometric probability model for protein identification and validation using tandem mass spectral data and protein sequence databases. , 2003, Analytical chemistry.

[25]  Alexey I Nesvizhskii,et al.  Interpretation of Shotgun Proteomic Data , 2005, Molecular & Cellular Proteomics.

[26]  K. Resing,et al.  Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics. , 2004, Analytical chemistry.

[27]  B. Searle,et al.  High-throughput identification of proteins and unanticipated sequence modifications using a mass-based alignment algorithm for MS/MS de novo sequencing results. , 2004, Analytical chemistry.

[28]  Eugene A. Kapp,et al.  Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly‐available database , 2005, Proteomics.

[29]  R. Aebersold,et al.  Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data , 2006, Molecular & Cellular Proteomics.

[30]  Michael Y. Galperin,et al.  The COG database: new developments in phylogenetic classification of proteins from complete genomes , 2001, Nucleic Acids Res..

[31]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[32]  E. Birney,et al.  The International Protein Index: An integrated database for proteomics experiments , 2004, Proteomics.

[33]  R. Aebersold,et al.  Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS. , 2004, Drug discovery today.