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BioMed Research International
Volume 2017, Article ID 5761517, 5 pages
https://doi.org/10.1155/2017/5761517
Research Article

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

1Center for Informational Biology, University of Electronic Science and Technology of China, Sichuan, China
2Key Laboratory for Neuroinformation of Ministry of Education, Chengdu 611731, China

Correspondence should be addressed to Jian Huang; nc.ude.ctseu@jh

Received 13 July 2017; Accepted 2 November 2017; Published 27 December 2017

Academic Editor: Rituraj Purohit

Copyright © 2017 Ning Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.