Recent Advances and Future Challenges in Predictive Modelling of Metalloproteins by Artificial Intelligence.

Abstract

Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sites and their functional roles in metalloproteins. State-of-the-art computational models developed for metal-binding sites seamlessly integrate protein sequence and structural data to unravel the complexities of metal coordination environments. Our goal in this mini-review is to give an overview of these tools and highlight the current challenges (predicting dynamic metal-binding sites, determining functional metalation states, and designing intricate coordination networks) remaining in the predictive models of metal-binding sites. Addressing these challenges will not only deepen our knowledge of natural metalloproteins but also accelerate the development of artificial metalloproteins with novel and precisely engineered functionalities.