A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Cong Zhou,Jianfeng Feng,Lucas D Bowler

doi:10.1186/1471-2105-9-325

Abstract

BackgroundA better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.ResultsIn this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the variances that can be used to tolerate noises and system biases within experimental MS/MS spectra.ConclusionThe intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification.

Highlights

A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS)
Subsets of these ions can be selected on the basis of their mass-to-charge ratio (m/z) and subject to further fragmentation, most commonly using collision induced dissociation (CID), in a process known as tandem mass spectrometry (MS/MS)
When a free proton is available within a peptide, we unsurprisingly find that proline (P) has a significant influence on fragmentation

Summary

Introduction

A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification. The derived peptides are subsequently ionized at entry into the mass spectrometer and measured as intact fragment (parent) ions. Subsets of these ions can be selected on the basis of their mass-to-charge ratio (m/z) and subject to further fragmentation, most commonly using collision induced dissociation (CID), in a process known as tandem mass spectrometry (MS/MS). Identification of the protein components in an analyzed sample can be achieved by correlating the observed signature spectra of individual peptides with the predicted MS/MS spectra of the amino acid sequences derived from protein databases such as Swiss-Prot and TrEMBL http://www.ebi.ac.uk/

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