Probing the Molecular Mechanism of Rifampin Resistance Caused by the Point Mutations S456L and D441V on Mycobacterium tuberculosis RNA Polymerase through Gaussian Accelerated Molecular Dynamics Simulation

A genetically encoded BRET-based SARS-CoV-2 Mpro protease activity sensor

The main protease, Mpro, is critical for SARS-CoV-2 replication and an appealing target for designing anti-SARS-CoV-2 agents. Therefore, there is a demand for the development of improved sensors to monitor its activity. Here, we report a pair of genetically encoded, bioluminescence resonance energy transfer (BRET)-based sensors for detecting Mpro proteolytic activity in live cells as well as in vitro. The sensors were generated by sandwiching peptides containing the Mpro N-terminal autocleavage sites, either AVLQSGFR (short) or KTSAVLQSGFRKME (long), in between the mNeonGreen and NanoLuc proteins. Co-expression of the sensors with Mpro in live cells resulted in their cleavage while mutation of the critical C145 residue (C145A) in Mpro completely abrogated their cleavage. Additionally, the sensors recapitulated the inhibition of Mpro by the well-characterized pharmacological agent GC376. Further, in vitro assays with the BRET-based Mpro sensors revealed a molecular crowding-mediated increase in the rate of Mpro activity and a decrease in the inhibitory potential of GC376. The sensors developed here will find direct utility in studies related to drug discovery targeting the SARS-CoV-2 Mpro and functional genomics application to determine the effect of sequence variation in Mpro.

Theoretical investigations on the effects of mutations in important residues of NS1B on its RNA-binding using molecular dynamics simulations

NS1B protein plays an important role in countering host antiviral defense and virulence of influenza virus B, considered as the promising target. The first experimental structure of the NS1B protein has recently been determined, was able to bind to double-stranded RNA (dsRNA). However, few studies attempt to investigate the RNA-binding mechanism of the NS1B. In this study, we provide our understanding of the structure-function relationship, dynamics and RNA-binding mechanism of the NS1B protein by performing molecular dynamics simulations combined and MM-GBSA calculations on the NS1B-dsRNA complex. 12 key residues are identified for RNA-binding by forming hydrogen bonds with the. Our results also demonstrate that mutations (R156A, K160A, R208A and K221A) can cause the local structure changes of NS1B CTD and the hydrogen bonds between NS1B CTD and RNA disappearance, which may be the main reasons for the decrease in RNA-binding affinity. These results mentioned will help us understanding the RNA-binding mechanism and could provide some medicinal chemistry insights chances for rational drug design targeting NS1B protein.

The Structural Basis of Mycobacterium tuberculosis RpoB Drug-Resistant Clinical Mutations on Rifampicin Drug Binding

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein-ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust "global" and "local" level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.