PrOMet: Accurate and Robust Metal-Binding Site and Type Prediction

Abstract

Metal binding to proteins is crucial for many biological functions, yet the prediction of metal-binding sites and metal types remains underexplored compared to other biomolecular interactions. Despite advances in deep learning, computational methods have achieved limited success in this area; even the best-reported approaches reach only moderate localization accuracy on high-resolution crystal structures, and even worse when applied to medium-resolution data. We hypothesize that the main challenge lies in the nature of metal binding. While the putative sites span the entire protein surface, actual binding requires the atomic-level coordination geometry. We present a structure-based metal binding site and type predictor, PrOMet, in this work, which leverages a Prune-and-Optimization strategy built upon two hierarchically connected machine learning architectures. This strategy allows the method to effectively narrow the search space while maintaining robustness to AlphaFold models and medium-resolution cryo-EM structures. PrOMet outperforms existing methods, including structure-based and language model-based predictors, achieving a PR-AUC of 0.739 for metal ion localization and an average F1-score of 0.743 for metal type classification-Zn, Ca, Mg, Mn, Fe, Cu, respectively. PrOMet can be an effective tool for allocating uncertain metal positions and types within protein structures, as well as for metalloenzyme design studies.

Abstract of "PrOMet: Accurate and Robust Metal-Binding Site and Type Prediction" by Park G, Seok C, Park H, https://doi.org/10.1002/advs.78003, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.