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.
We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically...
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, AlphaFold's 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.
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