Toward in Silico Biomolecular Manipulation through Static Modes: Atomic Scale Characterization of HIV-1 Protease Flexibility

Abstract

Probing biomolecular flexibility with atomic-scale resolution is a challenging task in current computational biology for fundamental understanding and prediction of biomolecular interactions and associated functions. This paper makes use of the static mode method to study HIV-1 protease considered as a model system to investigate the full biomolecular flexibility at the atomic scale, the screening of active site biomechanical properties, the blind prediction of allosteric sites, and the design of multisite strategies to target deformations of interest. Relying on this single calculation run of static modes, we demonstrate that in silico predictive design of an infinite set of complex excitation fields is reachable, thanks to the storage of the static modes in a data bank that can be used to mimic single or multiatom contact and efficiently anticipate conformational changes arising from external stimuli. All along this article, we compare our results to data previously published and propose a guideline for efficient, predictive, and custom in silico experiments.

Unraveling HIV protease flaps dynamics by Constant pH Molecular Dynamics simulations

“…Previous in silico studies (Brut et al, 2014;Piana et al, 2002;Pietrucci et al, 2009;Rick et al, 1998;and Tiefenbrunn et al, 2013) reveal that protease flexibility modulates the activation free-energy barrier of the enzymatic cleavage reaction. Furthermore, experimental studies have shown that there is a relationship between conformational sampling and drug resistance in HIV-PR (Cai et al, 2014;Cai et al 2012Foulkes-Murzycki et al, 2013Gibbs, 2014 andde Vera et al, 2013).…”

Enhancing DFT-based energy landscape exploration by coupling quantum mechanics and static modes

“…Such perturbations can be designed by the user to address specific questions, as discussed later. In this manner, the SM approach was validated and applied to various biological systems, SM being used to simulate protein folding upon electrostatic interactions, 9 active site stability upon mutations, 10 conformational change upon ligand binding, 11 and molecular properties upon functionalization, 12 to name but a few. Through these different cases, the SM predictive nature has been consolidated and shown to be associated with a very low computational cost, offering a versatile range of possibilities.…”