A comprehensive review of computation-based metal-binding prediction approaches at the residue level

Ye, N., Zhou, F., Liang, X., Chai, H., Fan, J., Li, B. and Zhang, J. (2022) A comprehensive review of computation-based metal-binding prediction approaches at the residue level. BioMed Research International, 2022, 8965712. (doi: 10.1155/2022/8965712) (PMID:35402609) (PMCID:PMC8989566)

Abstract

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China (Nos. 62002307 and 61802329), by the Innovation Team Support Plan of University Science and Technology of Henan Province (No. 19IRTSTHN014), by the Key Scientific Research Project of Colleges and Universities in Henan Province (Grant No. 22A170019), by the Project of Science and Technology Department of Henan Province (No. 212102210392), and by Nanhu Scholars Program for Young Scholars of Xinyang Normal University
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Chai, Haiting
• Authors: Ye, N., Zhou, F., Liang, X., Chai, H., Fan, J., Li, B., and Zhang, J.
• College/School: College of Medical Veterinary and Life Sciences
• Journal Name: BioMed Research International
• Publisher: Hindawi Publishing Corporation
• ISSN: 2314-6133
• ISSN (Online): 2314-6141
• Copyright Holders: Copyright © 2022 Nan Ye et al.
• First Published: First published in BioMed Research International 2022: 8965712
• Publisher Policy: Reproduced under a Creative Commons License