PTMClust is a software that can be applied to the output of blind PTM (Post-translational Modification) 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. Additionally, PTMClust markedly outforms another PTM refinement algorithm, PTMFinder. We demonstrate that 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.
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Bioinformatics. 2011 Mar 15;27(6):797-806. Epub 2011 Jan 22.
Computational refinement of post-translational modifications predicted from tandem mass spectrometry.
Chung C, Liu J, Emili A, Frey BJ.