CRiSP: Accurate structure prediction of disulfide-rich peptides with cystine-specific sequence alignment and machine learning

Abstract

Motivation: High-throughput sequencing discovers many naturally occurring disulfide-rich peptides or cystine-rich peptides (CRPs) with diversified bioactivities. However, their structure information, which is very important to peptide drug discovery, is still very limited. Results: We have developed a CRP-specific structure prediction method called Cystine-Rich peptide Structure Prediction (CRiSP), based on a customized template database with cystine-specific sequence alignment and three machine-learning predictors. The modeling accuracy is significantly better than several popular general-purpose structure modeling methods, and our CRiSP can provide useful model quality estimations. Contact: wuyd@pkusz.edu.cn or jiangfan@pku.edu.cn