Probe-based identification of metal-binding sites using deep learning representations.

Authors

Shijie Xu, Akira Onoda

Published in

Nature communications. Jun 30, 2026. Epub Jun 30, 2026.

Abstract

Metalloproteins are essential to many cellular processes. They use metal ions as cofactors to catalyze reactions, stabilize protein structures, and mediate electron transfer. Identifying their metal-binding sites remains difficult because of the complexity of protein environments and the promiscuous binding of metal ions, and existing computational methods are limited by accuracy and data scarcity. Here we introduce PRIME, a hybrid deep learning framework that combines evolutionary and structural signals to predict metal-binding sites accurately and efficiently. PRIME employs protein language models and pre-trained structure models to extract information from protein sequences and structures, together with a probe generation algorithm that bridges sequence- and structure-based predictions by scanning candidate sites. PRIME outperforms existing methods across diverse metal ions, from abundant zinc and calcium to challenging potassium and sodium. Ablation analysis shows that pretrained structure models improve accuracy. Case studies on AlphaFold2 models further demonstrate PRIME's potential for high-throughput metalloproteomics.

PMID:
42380105
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.