Ranking protein-peptide binding affinities with protein language models
In this study we explore the use of protein language models for ranking protein-peptide interaction strength, extending the concept of binary protein interaction classification. We introduce a method that measures and ranks protein binding affinities in an unsupervised manner, eliminating the need for extensive labeled data, structural information, or complex biochemical features. We demonstrate the utility of our approach across five distinct protein-peptide datasets by comparing predicted interaction strength rankings with experimentally derived inhibitory concentration (IC50) values. Furthermore, we discuss limitations encountered during our study and present preliminary findings on extending our approach to more general protein-protein interactions. Finally, we highlight the need for comprehensive datasets specifically designed for ranking protein-protein interactions.