Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Dai, Xin; Henderson, Max; Yoo, Shinjae; Liu, Qun

doi:10.1016/j.jmb.2025.168962

Title: Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Journal Article · 27 January 2025 · Journal of Molecular Biology

DOI: https://doi.org/10.1016/j.jmb.2025.168962 · OSTI ID:2507436

Dai, Xin ^[1]; Henderson, Max ^[2]; Yoo, Shinjae ^[1]; Liu, Qun ^[3]

Brookhaven National Laboratory (BNL), Upton, NY (United States)
Stony Brook Univ., NY (United States)
Stony Brook Univ., NY (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0012704

OSTI ID:: 2507436

Report Number(s):: BNL--227532-2025-JAAM

Journal Information:: Journal of Molecular Biology, Journal Name: Journal of Molecular Biology Journal Issue: 6 Vol. 437; ISSN 0022-2836

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
Evolutionary
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minimization
modeling
prediction
protein function
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