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

Haohuan Li | Zendy; Jing-Hao Hu | Zendy; Fan Jiang | Zendy; YunDong Wu | Zendy

Open Access

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

Author(s) -

Haohuan Li,

Jing-Hao Hu,

Fan Jiang,

YunDong Wu

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa193

Subject(s) - cystine , computer science , disulfide bond , sequence (biology) , artificial intelligence , protein structure prediction , machine learning , computational biology , chemistry , protein structure , biochemistry , cysteine , biology , enzyme

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.

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