Has AlphaFold 3 Solved the Protein Folding Problem for D-Peptides?
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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.
Summary
A crucial task in computational protein design is predicting fold, as this property determines the structure and function of a protein. Abramson et al. 1 published in Nature on AlphaFold 3 (AF3), a powerful deep learning framework for predicting chemical structures in both bound and unbound states. This architecture is tuned to respect chiral centers, which are atoms (in proteins, backbone α -carbons) covalently bound to four different chemical species 2 . These centers adopt two non-superposable forms, often called “handedness,” termed L (all biological proteins adopt this form) and D (the mirror image of L). L and D chiral centers exert significant influence on chemical function; changing the chirality of even a single residue can dramatically alter chemical properties such as enantioselective binding (e.g., antifolate resistance 3 ) and stability 4 . Additionally, D-peptides (small proteins containing exclusively D chiral centers) exhibit many advantages compared to their L-peptide counterparts, such as protease evasion 5 , and are therefore therapeutically relevant modalities. Due to vastly differing chemical properties, an algorithm should respect chiral center inputs and exhibit an error rate of 0%. Although Abramson et al. 1 reports a low 4.4% chirality violation across diverse chiral centers, we have found that the chiral violation rate for D-peptides with D chiral center inputs explicitly specified is much higher at 51%. Increasing the number of seeds fails to improve this rate. Our data highlights a crucial structural prediction error in AF3 and demonstrates the model is as accurate on average as chance (random chirality choice, L or D, for each peptide residue). Compared to empirical structures, AF3 is also highly inaccurate when folding and docking D-peptide:L-protein complexes. The failure of AF3 to accurately predict these chemical interactions indicates more work is need for high-quality prediction of D-peptides.
