ArticleScientific reports2017
Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advances in Multiple Reaction Monitoring (MRM) for Targeted Proteomics in Biomedical Research.Advances in experimental medicine and biology · 2026Review
- An Introduction to Advanced Targeted Acquisition Methods.Molecular & cellular proteomics : MCP · 2021Review
- Deep Learning in Proteomics.Proteomics · 2020Review
- Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis.Nature communications · 2020Article
- DART-ID increases single-cell proteome coverage.PLoS computational biology · 2019Article
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6 authors.
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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.
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