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dc.contributor.authorWang, Kai
dc.contributor.authorLyu, Nan
dc.contributor.authorDiao, Hongjuan
dc.contributor.authorJin, Shujuan
dc.contributor.authorZeng, Tao
dc.contributor.authorZhou, Yaoqi
dc.contributor.authorWu, Ruibo
dc.date.accessioned2020-05-19T02:34:20Z
dc.date.available2020-05-19T02:34:20Z
dc.date.issued2020
dc.identifier.issn1367-4803
dc.identifier.doi10.1093/bioinformatics/btaa292
dc.identifier.urihttp://hdl.handle.net/10072/393814
dc.description.abstractMOTIVATION: 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.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofjournalBioinformatics
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.titleGM-DockZn: A Geometry Matching based Docking Algorithm for Zinc Proteins
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWang, 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.dateAccepted2020-04-24
dc.date.updated2020-05-12T22:01:14Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis 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.hasfulltextFull Text
gro.griffith.authorZhou, Yaoqi


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