Ranking Peptide Binders by Affinity with AlphaFold

AlphaFold has revolutionized structural biology by predicting highly accurate structures of proteins and their complexes with peptides and other proteins. However, for protein‐peptide systems, we are also interested in identifying the highest affinity binder among a set of candidate peptides. We present a novel competitive binding assay using AlphaFold to predict structures of the receptor in the presence of two peptides. For systems in which the individual structures of the peptides are well predicted, the assay captures the higher affinity binder in the bound state, and the other peptide in the unbound form with statistical significance. We test the application on six protein receptors for which we have experimental binding affinities to several peptides. We find that the assay is best suited for identifying medium to strong peptide binders that adopt stable secondary structures upon binding. AlphaFold is transforming the field of protein structure prediction. Standing on its highly accurate mapping between protein sequence and structure, we discovered a novel way to predict the ranking of peptide binding affinities through a competitive binding assay. While there are still limitations, we anticipate this approach can be easily adopted for identifying higher affinity peptide binders.