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. AVAILABILITY AND IMPLEMENTATION The CRiSP server is freely available on the website at http//wulab.com.cn/CRISP. CONTACT wuyd@pkusz.edu.cn or jiangfan@pku.edu.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.