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Article 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.
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