RLDOCK: A New Method for Predicting RNA–Ligand Interactions

Abstract

The ability to accurately predict the binding site, binding pose, and binding affinity for ligand–RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand–RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand–RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand–RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA–ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand–RNA binding with an ensemble of RNA conformations and the metal-ion effects.

RNA–ligand molecular docking: Advances and challenges

“…This fact highlights the importance of blind docking (vs. local docking), where a small molecule is docked to the entire surface of the receptor without any prior knowledge of the binding site (see Figure 3). Although computational models—including AutoDock Vina, 66 GOLD, 59 and Glide 61 —or models originally developed for protein–ligand docking, but optimized for RNA targets—such as AutoDock, 98 ICM, 97 DOCK 6, 65 and FITTED 62 —can be adopted for RNA–ligand docking, methods developed specifically for RNA targets, such as RiboDock, 60 rDock, 67 MORDOR, 64 and RLDOCK, 70,71 have demonstrated advantages for RNA systems. See Table 1 for a list of docking software.…”

“…A complex of an aminoglycoside antibiotic, gentamicin (green) and the 16S‐rRNA A site of bacterial ribosome is used for illustration (PDB code: 2ET3 115 ). In this example, both docking (local and blind) processes are carried out using the RLDOCK model 70,71 . In local docking, the binding pocket is predefined and the sampling is contained within the red dashed box.…”

“…Several RNA–ligand docking software packages have incorporated their respective built‐in empirical scoring functions 60,67,70,71 specifically for RNA/DNA‐small molecule interactions. The scoring function of rDock, 67 the successor of the original model RiboDock, 60 contains a weighted sum of various intermolecular and intramolecular interaction energies, including the van der Waals potential (vdW), an empirical energy term for attractive and repulsive polar interactions and the desolvation energy.…”

“…RLDOCK 70,71 trained the weight coefficients for the different interaction terms such as van der Waals (vdW), electrostatic, polar and nonpolar hydration energies, and hydrogen‐bond interactions for a set of 30 RNA‐small molecule complexes. To enhance computational efficiency.…”

“…Third, the fact that we have a limited number of experimentally determined structures for RNA molecules and RNA–ligand complexes makes pure knowledge‐based approaches less effective for RNA–ligand predictions. In this regard, a physics‐based approach or a hybrid knowledge‐based and physics‐based approach can yield unique advantages 57,59–71 …”