Evaluation of AlphaFold2 structures as docking targets

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.

Automated summary

AlphaFold2 is a promising tool for predicting protein structures, but its predicted structures may not be suitable for docking. Researchers redocked ligands against experimental and AlphaFold2 structures to evaluate docking performance. The quality measure provided during structure prediction is not a good predictor of docking performance, but removing low-confidence regions and making side chains flexible improves the outcomes. Despite high-quality backbone prediction, fine structural details limit the use of AlphaFold2 models as docking targets.