MOTIVATION: A post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called blind) PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments. RESULTS: To address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We showed that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we showed that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Compared to another PTM refinement algorithm PTMFinder, PTMClust markedly outperforms it. We demonstrated our technique is able to reduce false PTM assignments, improve overall detection coverage, and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis. AVAILABILITY: Our algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://www.psi.toronto.edu/~cchung/PTMClust/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online or at http://www.psi.toronto.edu/~cchung/PTMClust/. CONTACT: email@example.com.
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