dc.contributor.author | Wang, Kai | |
dc.contributor.author | Lyu, Nan | |
dc.contributor.author | Diao, Hongjuan | |
dc.contributor.author | Jin, Shujuan | |
dc.contributor.author | Zeng, Tao | |
dc.contributor.author | Zhou, Yaoqi | |
dc.contributor.author | Wu, Ruibo | |
dc.date.accessioned | 2020-05-19T02:34:20Z | |
dc.date.available | 2020-05-19T02:34:20Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1367-4803 | |
dc.identifier.doi | 10.1093/bioinformatics/btaa292 | |
dc.identifier.uri | http://hdl.handle.net/10072/393814 | |
dc.description.abstract | MOTIVATION: Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino acid residues, metal ions, and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc coordination positions of 7 discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. RESULTS: GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a nonredundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross docking between different ligand complex structures of the same protein. The highest success rate for sampling neaest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Oxford University Press (OUP) | |
dc.relation.ispartofjournal | Bioinformatics | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.title | GM-DockZn: A Geometry Matching based Docking Algorithm for Zinc Proteins | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Wang, K; Lyu, N; Diao, H; Jin, S; Zeng, T; Zhou, Y; Wu, R, GM-DockZn: A Geometry Matching based Docking Algorithm for Zinc Proteins., Bioinformatics, 2020 | |
dcterms.dateAccepted | 2020-04-24 | |
dc.date.updated | 2020-05-12T22:01:14Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.description.notepublic | This publication was entered as an advanced online version. | |
gro.rights.copyright | © 2020 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version GM-DockZn: A Geometry Matching based Docking Algorithm for Zinc Proteins, Bioinformatics, 2020 is available online at: https://doi.org/10.1093/bioinformatics/btaa292. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Zhou, Yaoqi | |