BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures

Abstract

We developed BSP-SLIM, a new method for ligand-protein blind docking using low-resolution protein structures. For a given sequence, protein structures are first predicted by I-TASSER; putative ligand binding sites are transferred from holo-template structures which are analogous to the I-TASSER models; ligand-protein docking conformations are then constructed by shape and chemical match of ligand with the negative image of binding pockets. BSP-SLIM was tested on 71 ligand-protein complexes from the Astex diverse set where the protein structures were predicted by I-TASSER with an average RMSD 2.92 Å on the binding residues. Using I-TASSER models, the median ligand RMSD of BSP-SLIM docking is 3.99 Å which is 5.94 Å lower than that by AutoDock; the median binding-site error by BSP-SLIM is 1.77 Å which is 6.23 Å lower than that by AutoDock and 3.43 Å lower than that by LIGSITE(CSC) . Compared to the models using crystal protein structures, the median ligand RMSD by BSP-SLIM using I-TASSER models increases by 0.87 Å, while that by AutoDock increases by 8.41 Å; the median binding-site error by BSP-SLIM increase by 0.69Å while that by AutoDock and LIGSITE(CSC) increases by 7.31 Å and 1.41 Å, respectively. As case studies, BSP-SLIM was used in virtual screening for six target proteins, which prioritized actives of 25% and 50% in the top 9.2% and 17% of the library on average, respectively. These results demonstrate the usefulness of the template-based coarse-grained algorithms in the low-resolution ligand-protein docking and drug-screening. An on-line BSP-SLIM server is freely available at http://zhanglab.ccmb.med.umich.edu/BSP-SLIM.

Figure 1

Overview of the BSP-SLIM methodology.

Figure 2

Schematic representation of the procedures used to generate the negative images of a predicted binding site.

Figure 3

Summary of ligand binding modeling results by BSP-SLIM, SLIM, LIGSITE^CSC, and AutoDock. (A), percentage of targets vs. binding-site errors using I-TASSER protein models. (B), percentage of targets vs. binding-site errors using crystal protein structures. (C), percentage of targets vs. ligand RMSD using I-TASSER protein models. (D), percentage of targets vs. ligand RMSD using crystal protein structures. AutoDock (10) and AutoDock (100) mean that the AutoDock docking simulations consisted of 10 and 100 docking runs, respectively. The binding-site error and ligand RMSD were presented using the best of top five prediction results. Dashed lines depict the cutoff distance for estimating the success rate.

Figure 4

Number of predicted ligand binding sites versus the minimum binding-site errors. The minimum binding-site error for a given target protein was determined by the closest distance of all predicted binding sites from the geometric center of the native ligand. (A) Crystal structures. (B) I-TASSER models.

Figure 5

Comparison of negative images generated at two different binding sites. The binding site is displayed as red spheres. The illustrated figures were prepared using the PDB entry 1IA1. (A) The geometric center of the cognate ligand in the holo-structure was used as the coordinates of the binding site. (B) The binding site was translated by 5 Å in X, Y, and Z direction from the geometric center of the cognate ligand. The receptor and ligand are shown in a ribbon and a stick representation, respectively. The extracted negative images are displayed as a mesh representation.

Figure 6

Examples of docking poses successfully generated by BSP-SLIM using the I-TASSER predicted model structures. The native and docked ligands are shown in a stick representation colored gray and black, respectively. Crystal protein structures are displayed as gray lines. The PDB entries of the target holo-structures used for these figures are (A) 1P62, (B) 1XOQ, (C) 1HP0, and (D) 1V48.

Figure 7

The structures of the I-TASSER models used for large-scale virtual screening validation. The overall structures of the models are displayed by a ribbon representation. Ligand structures shown in a stick representation were transferred from holo-crystal structures of each target upon the structure superposition. Predicted ligand binding sites by BSP-SLIM are also displayed by green spheres.

Figure 8

ROC plot validation of BSP-SLIM blind virtual screening on CDK2, EGFr, FGFr1, PDE5, Thrombin and TK model.

Figure 9

(A) Docking performance comparison of BSP-SLIM with TLBD. Percentage of targets is plotted in terms of ligand RMSD using I-TASSER protein models. (B) Comparison of the number of steric clashes between ligand and receptor heavy atoms.

Figure 10

The ligand RMSDs by (A) BSP-SLIM and (B) TLBD as a function of similarity scores of the target ligand to the template ligand producing the ligand RMSD. A correlation of the ligand RMSDs with the similarity scores was determined by Pearson product moment correlation coefficient. (C) The percentage of successful targets plotted in terms of similarity score cutoff.

Figure 11

Application of the TLBD method in a large-scale EGFr virtual screening experiment (100 actives and 120,160 Asinex compounds). TLBD (−0.1): the best similarity scores between template ligands and active compounds were artificially reduced by 0.1.