Name
Title | ||
|---|---|---|
AutoDock CrankPep: Combining folding and docking to predict protein-peptide complexes. |
Abstract | ||
|---|---|---|
Motivation: Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. Results: Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz459 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Docking (dog),Computer science,Peptide,Computational biology,AutoDock | Journal | 35 |
Issue | ISSN | Citations |
24 | 1367-4803 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuqi Zhang | 1 | 1 | 0.70 |
| Michel F. Sanner | 2 | 431 | 29.79 |