Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction

Abstract

Protein-ligand interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-ligand interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others aim for multiple types but fail to effectively utilize information across different interactions, leading to increased complexity and inefficiency. This study presents a novel deep learning model named MucLiPred and a dual contrastive learning mechanism aimed at improving the prediction of multiple ligand-protein interactions and the identification of potential ligand-binding residues. We proposed two novel contrastive learning paradigms at residue and type levels, training the discriminative representation of samples. The residue-level contrastive learning hones in on distinguishing binding from non-binding residues with precision, shedding light on nuanced local interactions. In contrast, the type-level contrastive learning delves into the overarching context of ligand types, ensuring that representations of identical ligand types gravitate closer in the representational space and bolstering the models proficiency in discerning interaction motifs, enhancing the models ability to recognize global interaction patterns. This approach culminates in nuanced multi-ligand predictions, unraveling relationships between various ligand types, and fortifying the potential for precise protein-ligand interaction predictions. Empirical findings underscore MucLiPreds dominance over existing models, highlighting its robustness and unparalleled prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential ligand-binding residues with precision. By optimizing the models structure, we discovered that separating representation and classification tasks, leads to improved performance. Consequently, MucLiPred stands out as a groundbreaking tool in protein-ligand interaction prediction, laying the groundwork for future endeavors in this complex arena.