CLAPE: Protein-Ligand Binding Site Prediction via Protein Language Models

Protein-ligand interactions are fundamental to critical biological processes such as transcription, translation, and drug-target interaction. The precise identification of protein-ligand binding residues is essential for accurately modeling these interactions and facilitating subsequent research. However, the development of computational methods that are both efficient and accurate for this purpose remains a significant challenge. Progress in this field holds the potential to drive substantial advancements in biotechnology and drug discovery. In response to this need, we have recently developed a method known as Contrastive Learning And Pretrained Encoder (CLAPE). This approach integrates a pretrained protein language model with contrastive learning to predict ligand-binding residues. In this chapter, we provided a detailed overview of the CLAPE framework, including instructions for using the associated Python package and command-line tools. Additionally, we outline the methods employed for result visualization and describe the comprehensive processes involved in model training.