Enhanced Peptide Mass Fingerprinting through High Mass Accuracy: Exclusion of Non-Peptide Signals Based on Residual Mass

Abstract

Peptide mass fingerprinting (PMF) is among the principle methods of contemporary proteomic analysis. While PMF is routinely practiced in many laboratories, the complexity of protein tryptic digests is such that PMF based on unrefined mass spectrometric peak lists is often inconclusive. A number of data processing strategies have thus been designed to improve the quality of PMF peak lists, and the development of increasingly elaborate tools for PMF data reduction remains an active area of research. In this report, a novel and direct means of PMF peak list enhancement is suggested. Since the monoisotopic mass of a peptide must fall within a predictable range of residual values, PMF peak lists can in principle be relieved of many non-peptide signals solely on the basis of accurately determined monoisotopic mass. The calculations involved are relatively simple, making implementation of this scheme computationally facile. When this procedure for peak list processing was used, the large number of unassigned masses typical of PMF peak lists was considerably attenuated. As a result, protein identifications could be made with greater confidence and improved discrimination as compared to PMF queries submitted with raw peak lists. Importantly, this scheme for removal of non-peptide masses was found to conserve peptides bearing various post-translational and artificial modifications. All PMF experiments discussed here were performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), which provided the high mass resolution and high mass accuracy essential for this application. Previously reported equations relating the nominal peptide mass to the permissible range of fractional peptide masses were slightly modified for this application, and these adjustments have been illustrated in detail. The role of mass accuracy in application of this scheme has also been explored.