SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database.
Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.
Bafna
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V
Informatics Research, Celera Genomics, 45 W. Gude Drive, Rockville, MD 20850, USA. Vineet.Bafna@Celera.Com
Edwards
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N
D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010446:Peptide Fragments / Q000737:chemistry / Q000235:genetics / Q000302:isolation & purification; D010455:Peptides / Q000737:chemistry* / Q000235:genetics / Q000302:isolation & purification; D020543:Proteome; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000706:statistics & numerical data; D013269:Stochastic Processes
D010446:Peptide Fragments; D010455:Peptides; D020543:Proteome