AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms

Abstract

Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms.

Results: We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects.

Availability and implementation: http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

High affinity points in the hydrophobic affinity map (C), and hydrogen bond forming affinity maps (acceptor – OA; donor – HD) are selected, combined and clustered to yield putative ligand binding sites (Color version of this figure is available at Bioinformatics online.)

Fig. 2.

Fill points are segregated into hydrophobic points (C-green), and hydrogen bond acceptor (OA-red) and donor hydrogen points (HD-yellow). K-means clustering is performed on the C points and the resulting cluster centroids are kept as the hydrophobic feature points. Feature point extraction for O and H is performed using the receptor surface hydrogen-bonding atoms (Color version of this figure is available at Bioinformatics online.)

Fig. 3.

(A) Percentage of fills as a function of their fill-ligand Jaccard coefficients. (B) AutoSite and AutoLigand fill sizes comparison for the 85 systems. The inset plot shows the histogram of the differences in fill sizes. (C) Percentage of ligand atoms that find a fill point within 1Å, 1.5Å and 2Å. The ‘Unlabeled fill’ row provides the percentage of ligand atoms with a fill point with any label within the distance cut-offs. The rows below provide the percentages when fill labels are considered (Color version of this figure is available at Bioinformatics online.)

Fig. 4.

Predicted cluster and extracted feature points for streptavidin (PDB: 1stp; A, B) and HIV-1 protease (PDB: 1hps; C, D) overlaid with their respective experimentally determined bound ligand (balls and sticks; carbon - green). The potential hydrogen acceptor positions are shown as red spheres, hydrogen positions are shown in yellow spheres, and the hydrophobic positions are shown as green spheres (Color version of this figure is available at Bioinformatics online.)

Fig. 5.

(A) Predicted feature points represented as spheres (carbon – green; oxygen – red; and hydrogen – yellow) overlaid with 4d2w bound fragment (stick and balls; carbon – green). B) Fragment optimized inhibitor (stick and balls; carbon – purple) overlaid with the fragment and the predicted feature points for 4d2w. The crystallographic water molecules are shown as small red spheres. C) Schematic illustration of (B): Receptor (R: atoms - orange) interactions (arrows) are used to extract the feature points represented as circles (green - carbon; red - oxygen; yellow- hydrogen). Predicted feature points capture the ligand atoms (L: atoms - purple) and water molecules (cyan) (Color version of this figure is available at Bioinformatics online.)