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

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

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11953438 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature