RNA/peptide binding driven by electrostatics -- Insight from bi-directional pulling simulations

TL;DR

This paper introduces a novel computational approach using bi-directional pulling simulations and electrostatic interaction estimates to accurately predict RNA/peptide binding sites, poses, and affinities in explicit solvent molecular dynamics.

Contribution

The study presents a new simulation method that enhances sampling of electrostatics-driven RNA/peptide binding without compromising accuracy, applicable to various biomolecular interactions.

Findings

Successfully predicted binding pocket and pose for TAR RNA and cyclic peptide

Accurately estimated binding affinity through the method

Applicable to other electrostatic-driven binding events

Abstract

RNA/protein interactions play crucial roles in controlling gene expression. They are becoming important targets for pharmaceutical applications. Due to RNA flexibility and to the strength of electrostatic interactions, standard docking methods are insufficient. We here present a computational method which allows studying the binding of RNA molecules and charged peptides with atomistic, explicit-solvent molecular dynamics. In our method, a suitable estimate of the electrostatic interaction is used as an order parameter (collective variable) which is then accelerated using bi-directional pulling simulations. Since the electrostatic interaction is only used to enhance the sampling, the approximations used to compute it do not affect the final accuracy. The method is employed to characterize the binding of TAR RNA from HIV-1 and a small cyclic peptide. Our simulation protocol allows blindly predicting the binding pocket and pose as well as the binding affinity. The method is general and could be applied to study other electrostatics-driven binding events.