Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.

Abstract

Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications. We present a novel probabilistic scoring algorithm called Context-Sensitive Peptide Identification (CSPI) based on highly flexible Input-Output Hidden Markov Models (IO-HMM) that capture the influence of peptide physicochemical properties on their observed MS/MS spectra. We use several local and global properties of peptides and their fragment ions from literature. Comparison with two popular algorithms, Crux (re-implementation of SEQUEST) and X!Tandem, on multiple datasets of varying complexity, shows that peptide identification scores from our models are able to achieve greater discrimination between true and false peptides, identifying up to ∼25% more peptides at a False Discovery Rate (FDR) of 1%. We evaluated two alternative normalization schemes for fragment ion-intensities, a global rank-based and a local window-based. Our results indicate the importance of appropriate normalization methods for learning superior models. Further, combining our scores with Crux using a state-of-the-art procedure, Percolator, we demonstrate the utility of using scoring features from intensity-based models, identifying ∼4-8 % additional identifications over Percolator at 1% FDR. IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data.