AlphaFold encodes the principles to identify high affinity peptide binders

Abstract

Machine learning has revolutionized structural biology by solving the problem of predicting structures from sequence information. The community is pushing the limits of interpretability and application of these algorithms beyond their original objective. Already, AlphaFolds ability to predict bound conformations for complexes has surpassed the performance of docking methods, especially for protein-peptide binding. A key question is the ability of these methods to differentiate binding affinities between several peptides that bind the same receptor. We show a novel application of AlphaFold for competitive binding of different peptides to the same receptor. For systems in which the individual structures of the peptides are well predicted, predictions in which both peptides are introduced capture the stronger binder in the bound state, and the other peptide in the unbound form. The speed and robustness of the method will be a game changer to screen large libraries of peptide sequences to prioritize for detailed experimental characterization.