Benchmarking HelixFold3-Predicted Holo Structures for Relative Free Energy Perturbation Calculations

Abstract

AlphaFold2 demonstrated remarkable capabilities for protein structure prediction. However, it is limited to downstream tasks, such as ligand docking and free energy calculations, as it cannot predict holo structures with bound ligands. AlphaFold3, a state-of-the-art protein structure prediction model, can predict the binding structures of complexes with proteins, nucleic acids, small molecules, ions, and modified residues with cutting-edge performance. However, AlphaFold3 does not currently provide access to some functions, such as the prediction of protein-ligand complex structures. To reduce the enormous costs in early small molecule drug discovery, verifying the utility of protein-ligand complex prediction methods, such as AlphaFold3, in downstream tasks like free energy perturbation calculations is crucial. In this study, we evaluated HelixFold3, designed to emulate AlphaFold3, in predicting holo and apo structures' complex formations and examined its utility in free energy perturbation calculations. Regarding the complex structure prediction performance of the 8 targets from Wang \etal's FEP benchmark, HelixFold3 showed superior performance to AlphaFold2 and existing methods. Predicting a holo structure rather than an apo structure resulted in higher binding site prediction accuracy. Furthermore, using HelixFold3 predicted structures in practical situations, where binding free energies of all derivatives were estimated, both structures achieved accuracies comparable to crystal structures. Additionally, novel derivatives not included in the training data were accurately predicted, demonstrating that free energy calculations using these novel structures are sufficiently usable.