PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides

作者：Ning Li, Juanjuan Kang, Lixu Jiang, Bifang He, Hao Lin, Jian Huang · 发表于：BioMed Research International · 年份：2017 · DOI：10.1155/2017/5761517 · 被引用次数：80 · 研究领域：vaccines and immunoinformatics approaches、Machine Learning in Bioinformatics、RNA and protein synthesis mechanisms

Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.