Basophile:Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

作     者：Dong Wang Surendra Dasari Matthew C.Chambers Jerry D.Holman Kan Chen Daniel C.Liebler Daniel J.Orton Samuel O.Purvine Matthew E.Monroe Chang Y.Chung Kristie L.Rose David L.Tabb 

作者机构：Department of Biomedical InformaticsVanderbilt University Medical Center Division of Biomedical Statistics and InformaticsMayo Clinic Department of BiochemistryVanderbilt University Medical Center Biological Sciences Division and Environmental Molecular Sciences LaboratoryPacific Northwest National Laboratory Department of PharmacologyVanderbilt University Medical Center Vanderbilt-Ingram Cancer CenterVanderbilt University School of Medicine 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2013年第11卷第2期

页      面：86-95页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 08[工学] 0817[工学-化学工程与技术] 0714[理学-统计学（可授理学、经济学学位）] 0703[理学-化学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Library of Medicine training grant (Grant No. 5T15LM007450-10) 

主　　题：Fragmentation Basicity Fragment size Ordinal regression 

摘      要：In shotgun proteomics, database search algorithms rely on fragmentation models to pre- dict fragment ions that should be observed for a given peptide sequence. The most widely used strat- egy （Naive model） is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmen- tation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher- energy collision induced dissociation （HCD） of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% （on average） over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.