Work

mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding

Year: 2026

Type: article

Abstract: Metal ions play critical structural, regulatory, and enzymatic roles in proteins, making their binding essential for biological processes. Experimental identification of metal-binding sites is resource-intensive and limited in scalability. Recent advances in protein language models have transformed computational predictions, yet current tools do not address how residue-level metal-binding probabilities change upon mutation. To fill this gap, mCSM-metal leverages embeddings from ESMBind with our graph-based structural signatures to accurately predict the effects of single or multiple point mutations on the binding of seven essential ions (Zn 2+ , Ca 2+ , Mg 2+ , Mn 2+ , Fe 3+ , Co 2+ , Cu 2+ ). Our model achieves accuracies, F1-scores, and Matthews Correlation Coefficient values up to 0.97, 0.97, and 0.95, outperforming other approaches. The webserver provides an interactive platform to assess and visualize local and long-range impacts of mutations on metal-ion binding, offering new avenues for applications in structural biology, disease modelling, and protein engineering. The web application is freely available at: https://biosig.lab.uq.edu.au/mcsm_metal/ .

Source: Journal of Molecular Biology

DOI: 10.1016/j.jmb.2026.169678

Authors: Akshita Kumar, Ashar J. Malik, David B. Ascher

Institutions: The University of Queensland, Baker Heart and Diabetes Institute, The University of Melbourne

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,730

Funders: State Government of Victoria, National Health and Medical Research Council