Kolmogorov−Smirnov Scores and Intrinsic Mass Tolerances for Peptide Mass Fingerprinting

Abstract

Peptide Mass Fingerprinting (PMF) uses proteolytic peptide masses and a prespecified search database to identify proteins. At the core of a PMF database search algorithm lies a quality statistic that gauges the level to which an experimentally obtained peak list agrees with a list of theoretically observable mass-to-charge ratios for a protein in a database. In this paper, we propose, implement and evaluate using a statistical (Kolmogorov-Smirnov-based) test computed for a large mass error threshold to avoid the choice of appropriate mass tolerance by the user. We use the mass tolerance identified by the Kolmogorov-Smirnov test for computing other quality measures. The results from our careful and extensive benchmarks using publicly available gold-standard data sets suggest that the new method of computing the quality statistics without requiring the end-user to select a mass tolerance is competitive. We investigate the similarity measures in terms of their information content and conclude that the similarity measures are complementary and can be combined into a scoring function to possibly improve upon the over all accuracy of PMF based identification methods.