MPLBind: Predicting the Effect of Binding Site Point Mutations on Protein–Ligand Binding Affinity Using Protein Large Language Models

Protein-ligand binding affinity may be affected by protein mutations, especially point mutations occurring within the binding site, which may indirectly contribute to interindividual differences in drug response. In recent years, several methods have been proposed to predict the effect of binding site mutations on protein-ligand binding affinity. However, the impact of mutations is difficult to predict accurately. In this study, a method named MPLBind is proposed to predict the effect of mutations on protein-ligand binding affinity, which effectively utilizes ligand descriptors and fingerprints, mutant residues' local environment changes, and large protein language model features, which contain context, evolutionary information, conservation, and functional information on protein sequences. The use of the large protein language model and the fusion strategy of ligand and mutation features significantly improved the prediction performance. Experimental results show that MPLBind has better performance against competing baseline models, not only in predicting protein-ligand binding affinity but also in predicting the effect of mutations on protein-ligand binding affinity.