PinMyMetal: a hybrid learning system to accurately model transition metal binding sites in macromolecules

Huihui Zhang, Juanhong Zhong, Michal Gucwa, Yishuai Zhang, Haojie Ma, Lei Deng, Longfei Mao, Wladek Minor (), Nasui Wang () and Heping Zheng ()
Additional contact information
Huihui Zhang: First Affiliated Hospital of Shantou University Medical College
Juanhong Zhong: Bioinformatics Center
Michal Gucwa: University of Virginia
Yishuai Zhang: Bioinformatics Center
Haojie Ma: Bioinformatics Center
Lei Deng: Hunan University
Longfei Mao: Bioinformatics Center
Wladek Minor: University of Virginia
Nasui Wang: First Affiliated Hospital of Shantou University Medical College
Heping Zheng: First Affiliated Hospital of Shantou University Medical College

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural, catalytic, or regulatory roles. Modeling transition metal ions, especially in transient, reversible, and concentration-dependent regulatory sites, remains challenging. We present PinMyMetal (PMM), a hybrid machine learning system designed to accurately predict transition metal localization and environment in macromolecules, tailored to tetrahedral and octahedral geometries. PMM outperforms other predictors, achieving high accuracy in ligand and coordinate predictions. It excels in predicting regulatory sites (median deviation 0.36 Å), demonstrating superior accuracy in locating catalytic sites (0.33 Å) and structural sites (0.19 Å). Each predicted site is assigned a certainty score based on local structural and physicochemical features, independent of homologs. Interactive validation through our server, CheckMyMetal, expands PMM’s scope, enabling it to pinpoint and validate diverse functional metal sites from different structure sources (predicted structures, cryo-EM, and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top . The PMM workflow can interrogate with protein sequence to characterize the localization of the most probable transition metals, which is often interchangeable and hard to differentiate by nature.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-57637-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57637-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-57637-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().