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.
Highlights
Phage display is a versatile and powerful technology to find ligands for any given target [1,2,3]
We have proposed a novel Polystyrene surface-binding peptides (PSBPs) predictor based on support vector machine (SVM) named PSBinder
The model built with optimized DPC (ODPC) attains the maximum accuracy of 87.02% and an impressive Matthews correlation coefficient (MCC) of about 0.74 (Table 1)
Summary
Phage display is a versatile and powerful technology to find ligands for any given target [1,2,3]. These targets can be a wide variety of substances, such as small molecules, proteins, glycan, cells, organs, and even whole organisms. A high affinity polystyrene surface-binding peptide (PSBP) can help to build a highly effective ELISA system and immobilize proteins or antibodies directly onto the polystyrene plates with minimal conformational changes [4,5,6,7,8]. PSBPs as the target-unrelated peptides (TUPs) are false positive results and may mislead the following experiments [9].
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