Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa193 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 11 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zi-Lin Liu | 1 | 0 | 0.34 |
| Jing-Hao Hu | 2 | 0 | 0.34 |
| Fan Jiang | 3 | 0 | 0.68 |
| Yun-Dong Wu | 4 | 8 | 3.43 |