Has AlphaFold 3 Solved the Protein Folding Problem for D-Peptides?

Due to the favorable chemical properties of mirrored chiral centers (such as improved stability, bioavailability, and membrane permeability) the computational design of D-peptides targeting biological L-proteins is a valuable area of research. To design these structures in silico, a computational workflow should correctly dock and fold a peptide while maintaining chiral centers. The latest AlphaFold 3 (AF3) from Abramson et al. (2024) enforces a strict chiral violation penalty to maintain chiral centers from model inputs and is reported to have a low chiral violation rate of only 4.4% on a PoseBusters benchmark containing diverse chiral molecules. Herein, we report the results of 3,255 experiments with AF3 to evaluate its ability to predict the fold, chirality, and binding pose of D-peptides in heterochiral complexes. Despite our inputs specifying explicit D-stereocenters, we report that the AF3 chiral violation for D-peptide binders is much higher at 51% across all evaluated predictions; on average the model is as accurate as chance (random chirality choice, L or D, for each peptide residue). Increasing the number of seeds failed to improve this violation rate. The AF3 predictions exhibit incorrect folds and binding poses, with D-peptides commonly oriented incorrectly in the L-protein binding pocket. Confidence metrics returned by AF3 also fail to distinguish predictions with low chirality violation and correct docking vs. predictions with high chirality violation and incorrect docking. We conclude that AF3 is a poor predictor of D-peptide chirality, fold, and binding pose. Finally, we propose solutions to improve this model. Significance StatementAlphaFold 3 (AF3) is a model trained to predict protein interactions. This algorithm is tuned to respect chiral centers (L and D). Changing the chirality of even one protein residue can significantly alter chemical properties such as binding and stability. Therefore, an algorithm should exhibit a chiral center error rate of 0%. Although the original AF3 authors reported a 4.4% chirality violation, we have found that the rate for D-peptides is much higher at [~]50%. Our data highlights a crucial structural prediction error in AF3 and demonstrates that this widely used model is as accurate on average as chance (random chirality choice, L or D, for each peptide residue). These results indicate structure prediction of D-peptides is an outstanding problem.