Abstract
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.
Highlights
Tuberculosis (TB) remains a major public health problem globally, with resistance to rifampicin (RIF) and isoniazid (INH), the main anti-TB first-line drugs, varying according to geographical location [1,2]
His451Pro, Ser456Gln, and Se456Trp exhibited larger Rg variations relative to WT, Asp441Val, Arg454Gln, and His451Gly. These results suggest that: (I) Asp441Val confers insignificant effects on the protein packing density, both globally and locally. (II) His451Pro and Ser456Gln remain mainly intact, except for looser packing in the rifampicin binding region. (III) The His451Gly and Arg454Gln mutations majorly influence the spatial packing of the entire structure. (IV) While structural compaction is enhanced globally in Ser456Trp, structural expansion is favored in the RIF binding region
Tuberculosis is a major health problem that is further exacerbated by the emergence of multidrugresistant tuberculosis (MDR-TB)
Summary
Tuberculosis (TB) remains a major public health problem globally, with resistance to rifampicin (RIF) and isoniazid (INH), the main anti-TB first-line drugs, varying according to geographical location [1,2]. Substitutions at position 451 (His451Ser/Met/Glut/Asp/Tyr/Arg) induced detrimental effects on protein–ligand stability, as well as affinity [27,28] While these studies have extensively explored thermodynamic aspects of RIF binding and have captured relevant biophysical interactions, less attention has been paid to conformation selection and intraprotein communication regulation. It is not clear whether residues within the RRDR region are key intermediaries in the intraprotein information flow, nor is it clear how mutations within this hotspot region influence the protein conformation freedom of the free energy landscape, dictating the drug binding kinetics. The visual inspection of the docked poses of the mutated rpoB complexes revealed that RIF either developed few hydrogen bond interactions with the binding sites residues or abolished them altogether (Figure 2). High ligand RMSD scores were yielded during the superimposition of the mutant models against the WT complex (Table S1)
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