Structural accuracy of modeled binding pockets.

Binding pockets are defined to include all amino acid residues with any atom within 5 Å of the ligand in an experimentally determined structure. We compute all-atom binding pocket RMSDs between each modeled structure and all experimentally determined structures of the same protein. For comparison, we also compute binding pocket RMSDs between all pairs of experimentally determined structures of the same protein with different ligands bound. The middle line of each box in the plot is the median RMSD, with the box extending from the 1st to the 3rd quartile and defining the “interquartile range.” Whiskers extend to last data points that are within 150% of the interquartile range, and outlier data points beyond those are shown individually.

Accuracy of ligand binding poses predicted by computational docking to AlphaFold 2 models, traditional template-based models, or protein structures determined experimentally in complex with a ligand different from the one being docked.

We plot the fraction of docked ligands whose pose is predicted correctly (see Methods). Error bars are 90% confidence intervals calculated via bootstrapping. *** for P values < 0.001. ns for P values > 0.05.

An example in which docking to an AF2 model yields poor results even though the model’s binding pocket has high structural accuracy. We predict the binding pose of the drug aprepitant to its target, the neurokinin-1 receptor (NK1R) given either the AF2 model (orange) of NK1R or the experimentally determined structure (blue) of NK1R bound to a different ligand, L760735. (A, B) The binding pocket of the AF2 model is more similar (lower RMSD) than the binding pocket of the L760735-bound structure to the binding pocket of the aprepitant-bound structure (the “reference structure,” white). Amino acid residues whose positions differ most from the reference structure are shown in sticks (see Methods). (C, D) The aprepitant binding pose predicted by docking is much less accurate (higher RMSD) when using the AF2 model than when using the L760735-bound structure. Ligand L76035 shares a scaffold with aprepitant; for completeness, we include another example with highly dissimilar ligands in Supplementary Figure S6.

An example in which docking to a traditional template-based model yields better results than docking to an AF2 model, even though the AF2 model’s binding pocket has higher structural accuracy. We predict the binding pose of the psychedelic LSD to its primary target, the serotonin 2A receptor (5HT2A) given either the AF2 model (orange) or a traditional model (green) of 5HT2A. (A, B) The binding pocket of the AF2 model is more similar (lower RMSD) than that of the traditional model to the binding pocket of the experimentally determined LSD-bound structure (the “reference structure,” white). Amino acid residues that differ most from the reference structure are shown in sticks. (C, D) The LSD binding pose predicted by docking is less accurate (higher RMSD) when using the AF2 model than when using the traditional model.

Pose prediction accuracy as a function of binding pocket structural accuracy when docking to AF2 models or experimentally determined structures.

Docking to an experimentally determined structure generally leads to more accurate pose prediction than docking to an AF2 model with the same binding pocket RMSD. The difference between the two curves is statistically significant for all binding pocket RMSD values below 1.1 Å (see Methods).