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.

预测蛋白质的配体结合位点对于理解蛋白质功能和筛选潜在药物是一项基础而重要的任务。通过使用蛋白序列和配体分子图作为输入，LaMPSite 模型能够在没有 3D 蛋白质结构信息的情况下预测结合位点，与需要 3D 结构的基准方法相比具有竞争力的性能，这为药物发现提供了新的机会。