RFpeptides designs high-affinity macrocyclic peptide binders from scratch for undruggable targets
David Baker's lab has extended its deep-learning toolkit — RoseTTAFold2 and RFdiffusion — into RFpeptides, a framework that generates macrocyclic peptide binders against arbitrary protein targets without any starting template. Testing 20 or fewer designed macrocycles against each of four structurally diverse proteins yielded binders with medium-to-high affinity in every case, including a sub-10 nanomolar binder generated purely from a predicted target structure. X-ray crystal structures of the bound complexes matched the computational models closely, with a Cα root-mean-square deviation under 1.5 angstroms.
This matters because macrocyclic peptides sit in a therapeutic sweet spot between small molecules and antibodies: large enough to grip flat, featureless protein surfaces that defeat small drugs, yet small enough to be synthesized chemically and, potentially, made cell-permeable. Reliable computational design collapses what used to be a slow, luck-dependent screening process into a matter of testing a handful of candidates.
A good PeptideWiki angle: a plain-language explainer on why macrocyclic peptides are the field's answer to "undruggable" targets, using RFpeptides as the worked example of how AI design now delivers nanomolar binders on the first try.