Protein Binding Site Prediction Using Deep Neural Networks

Metals play an essential role in a multitude of pathological, diagnostic, and physiological processes. Metal-binding proteins, also known as metalloproteins, are pivotal for various metabolic functions. The three-dimensional structures attained through protein folding reveal the critical functions they serve. Predicting metal-binding in proteins is a fundamental step in assigning functions to newly discovered proteins. This prediction is instrumental in acquiring functional insights during genomic studies, and it holds great significance in annotating protein functions and advancing drug discovery. Machine learning techniques propel computational predictions these methods applied to data derived from amino acid sequences are extensively employed in the fields of bioinformatics and protein metal-binding. Proteins rely on interactions with various ligands to carry out their functions, with metal ions being significant among these ligands. Currently, predicting the binding sites of metal ions on proteins presents a formidable challenge. Numerous machine learning algorithms have found widespread application in predicting protein-metal ion ligand binding residues. In recent times, integration of deep learning and natural language processing has opened the door to leveraging deep learning algorithms for protein binding site prediction. In this study, we propose an efficient deep neural network algorithm for predicting Mg2+ and Ca2+ ligand binding sites.