CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases.


Journal

Journal of molecular biology
ISSN: 1089-8638
Titre abrégé: J Mol Biol
Pays: Netherlands
ID NLM: 2985088R

Informations de publication

Date de publication:
15 07 2023
Historique:
received: 08 02 2023
revised: 13 04 2023
accepted: 27 04 2023
medline: 27 6 2023
pubmed: 26 6 2023
entrez: 25 6 2023
Statut: ppublish

Résumé

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

Identifiants

pubmed: 37356903
pii: S0022-2836(23)00219-X
doi: 10.1016/j.jmb.2023.168141
pii:
doi:

Substances chimiques

Ligands 0
Proteins 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

168141

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Shiwei Wang (S)

BMLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, PR China.

Juan Xie (J)

Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China.

Jianfeng Pei (J)

Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing 100871, PR China.

Luhua Lai (L)

BMLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, PR China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing 100871, PR China. Electronic address: lhlai@pku.edu.cn.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
Databases, Protein Protein Domains Protein Folding Proteins Deep Learning

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Adenosine Triphosphate Adenosine Diphosphate Mitochondrial ADP, ATP Translocases Binding Sites Mitochondria

Classifications MeSH