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

Liu, Zi-Lin
Hu, Jing-Hao
Jiang, Fan
Wu, Yun-Dong

¹Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
²College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
³NanoAI Biotech Co., Ltd, Shenzhen 518118, China
⁴Shenzhen Bay Laboratory, Shenzhen 518055, China

^†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

^*To whom correspondence should be addressed. Email: [email protected]

Received on November 16, 2019; revised on February 6, 2020; accepted on March 22, 2020; editorial decision on March 12, 2020

Associate Editor: Pier Luigi Martelli

Bioinformatics 36(11):p 3385-3392, June 1, 2020. | DOI: 10.1093/bioinformatics/btaa193

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:

[email protected] or [email protected]

Supplementary information:

Supplementary data are available at Bioinformatics online.