Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence

TL;DR

LaMPSite predicts protein ligand binding sites using only sequence data and ligand graphs, leveraging pre-trained language models and graph neural networks to achieve competitive results without 3D structures.

Contribution

Introduces LaMPSite, a novel method that predicts binding sites solely from protein sequences and ligand graphs, bypassing the need for 3D structures.

Findings

Achieves competitive performance with structure-based methods

Operates effectively without 3D coordinate information

Expands applicability to proteins lacking structural data

Abstract

Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery.