title: A point-process model for rapid identification of post-translational modifications.
abstract: Post-translational modifications (PTMs) are very important to biological function, and yet are notoriously difficult to detect and identify, especially in a high-throughput manner. Most of the existing approaches rely on exhaustive searches which are highly time consuming and thus are currently limited to handling of a few types of PTMs. In this paper, we present a point-process model that aims to find the optimal mass shifts to maximize the spectra alignment between an experimental MS/MS spectrum and a candidate theoretical spectrum, through cross-correlation calculation, yields a rapid search for all types of PTMs in a blind mode, i.e., without giving the types of the searching PTMs in advance. The test results show that our new approach's performance is comparable to or better than the other blind search methods, but is more efficient computationally and simpler in its concept.
authors.affiliation: Department of Biochemical and Molecular Biology, University of Georgia, GA, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Yan
authors.forename: Tong
authors.initials: T
authors.lastname: Zhou
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Liu
authors.forename: Vincent A
authors.initials: VA
authors.lastname: Emanuele
authors.forename: Victor
authors.initials: V
authors.lastname: Olman
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D008969:Molecular Sequence Data; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000706:statistics & numerical data; D053719:Tandem Mass Spectrometry / Q000706:statistics & numerical data
chemical_list: D011506:Proteins