Abstract：
:The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Support Vector Regression (SVR) algorithm. Corresponding to amino acid locus, each peptide sequence was converted to a featured locus vector consisting of zeros and ones. With locus vector information from LC-MS/MS data sets, an SVR computational process was trained and evaluated. LsRP finally provided a prediction correlation coefficient of 0.95~0.99. We compared our method with two common predictors. Results showed that LsRP outperforms these methods and tracked up to 30% extra peptides in an extraction RT window of 2 min. A new strategy by combining LsRP and calibration peptide approach was then proposed, which open up new opportunities for precision proteomics.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Lu W,Liu X,Liu S,Cao W,Zhang Y,Yang Pdoi
10.1038/srep43959subject
Has Abstractpub_date
2017-03-17 00:00:00pages
43959issn
2045-2322pii
srep43959journal_volume
7pub_type
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journal_title：Scientific reports
pub_type： 杂志文章
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pub_type： 杂志文章
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journal_title：Scientific reports
pub_type： 杂志文章
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