Affiliation:
1. Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China
2. College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China
3. Department of Chemistry, University of Oxford, Oxford, UK
Abstract
Abstract
Motivation
Metalloenzymes are attractive targets for therapeutic intervention owing to their central roles in various biological processes and pathological situations. The fast-growing body of structural data on metalloenzyme-ligand interactions is facilitating efficient drug discovery targeting metalloenzymes. However, there remains a shortage of specific databases that can provide centralized, interconnected information exclusive to metalloenzyme-ligand associations.
Results
We created a Metalloenzyme-Ligand Association Database (MeLAD), which is designed to provide curated structural data and information exclusive to metalloenzyme-ligand interactions, and more uniquely, present expanded associations that are represented by metal-binding pharmacophores (MBPs), metalloenzyme structural similarity (MeSIM) and ligand chemical similarity (LigSIM). MeLAD currently contains 6086 structurally resolved interactions of 1416 metalloenzymes with 3564 ligands, of which classical metal-binding, non-classical metal-binding, non-metal-binding and metal water-bridging interactions account for 63.0%, 2.3%, 34.4% and 0.3%, respectively. A total of 263 monodentate, 191 bidentate and 15 tridentate MBP chemotypes were included in MeLAD, which are linked to different active site metal ions and coordination modes. 3726 and 52 740 deductive metalloenzyme-ligand associations by MeSIM and LigSIM analyses, respectively, were included in MeLAD. An online server is provided for users to conduct metalloenzyme profiling prediction for small molecules of interest. MeLAD is searchable by multiple criteria, e.g. metalloenzyme name, ligand identifier, functional class, bioinorganic class, metal ion and metal-containing cofactor, which will serve as a valuable, integrative data source to foster metalloenzyme related research, particularly involved in drug discovery targeting metalloenzymes.
Availability and implementation
MeLAD is accessible at https://melad.ddtmlab.org.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
National Natural Science Foundation
Sichuan Science and Technology Program
Scientific Research Foundation of Sichuan University
Fundamental Research Funds for the Central Universities
Sichuan University Postdoctoral Interdisciplinary Innovation Startup Foundation
Cancer Research UK
Medical Research Council
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability