DOCK 6: combining techniques to model RNA-small molecule complexes

Abstract

With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA-ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate experimentally determined binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 A heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson-Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water molecules and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein-ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addition, in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.

FIGURE 1.

Effect of number of rotatable bonds on DOCKing success rate (●) (defined as the percent of test set with best scoring pose reproducing the experimental structures with 2 Å heavy-atom RMSD) and average length of DOCKing calculation (○) using sampling parameters optimized for proteins in DOCK 5.

FIGURE 2.

Diagram identifying rigid anchor (Layer 1) and flexible layers for growth.

FIGURE 3.

Analysis of reDOCKing and rescoring successes and failures. Successes (striped) and failures (open) are compared for DOCKing using the ranking-based sampling method with Grid Score and receptor in vacuum as (A) a function of the number of rotatable ligand bonds or (B) formal charge of the ligand. (C) Cumulative success rates for Gasteiger (▲), AM1BCC (▼), and RESP (■) charge models are compared as a function of the number of rotatable bonds for the AMBER score using the clustering-based sampling method with explicit water molecules and counterions. (D) Success rates for Gasteiger (horizontal stripes), AM1BCC (diagonal stripes), and RESP (solid) charge models are compared as a function of the ligand formal charge for the AMBER rescoring methodology. Success is defined by the top-scoring pose being within 2 Å heavy-atom RMSD from the experimental structure.

FIGURE 4.

Exploration of entire list of generated conformations. Success is defined as any pose in the cumulative list being within 2 Å heavy-atom RMSD from the experimental structure. (1A–4A) Gasteiger–Hückel, (1B–4B) AM1-BCC, and (1C–4C) RESP ligand charge models are compared for each analysis. (1) Cumulative success rates of clusterheads (lowest-scoring member of each cluster) for clustering-based sampling methods with receptor in vacuum. (2) Cumulative success rates for ranked list of conformations for ranking-based sampling method with receptor in vacuum. (3) Cumulative success rates of clusterheads for clustering-based sampling methods with the receptor plus explicit water molecules and counterions. (4) Cumulative success rates for ranked list of conformations for ranking-based sampling method with the receptor plus explicit water molecules and counterions. Test set is divided into less than seven (□) and less than 13 (△) rotatable bonds.

FIGURE 5.

Comparison of the success rates for reDOCKing of generated conformational ensembles for protein (closed symbols) and RNA test sets (open symbols). Success is defined as any pose in the cumulative list being within 2 Å heavy-atom RMSD from the experimental structure. All ligands have six or less rotatable bonds and AM1-BCC partial charges. Both sets were DOCKed using the clustering-based sampling algorithm. The protein test set (●) was DOCKed with receptors in a vacuum. The RNA test set was DOCKed both with receptors in a vacuum (□) and with two shells of explicit water molecules plus sodium counterions (◇).

FIGURE 6.

Effect of allowing increasing portions of receptor to move using the MDM protocol during rescoring with AMBER GB/SA Score. Ligand alone (0 Å) is compared to the ligand plus all residues within 1–7 Å of the spheres. Ligand charge models Gasteiger–Hückel (—), AM1-BCC (–), and RESP (···) were compared.