The 28th Congress of the South African Society for Biochemistry and Molecular Biology (SASBMB), hosted by the University of Venda, was held at the Protea Hotel Polokwane Ranch Resort from 7–10 July 2024. The theme of the congress was “Biochemistry leading the future.” The event featured several plenary presentations and parallel sessions focusing on a broad range of topics, including drug discovery, infectious and parasitic diseases, phytomedicine, non-communicable diseases, protein biochemistry, and computational and structural biology, among others. Here, we highlight key lectures that were presented by the plenary speakers whose presentations set the tone for this meeting. Overall, the congress was a resounding success.

Annexins are Ca2+ and membrane-binding proteins conserved across eukaryotes and involved in diverse membrane-associated processes, including unconventional protein secretion (UPS). In the intestinal parasite Giardia lamblia, annexins are represented by a family of 21 proteins termed alpha-giardins, all of which lack classical signal peptides yet are detected at the parasite surface and in the secretome. Here, we present a comprehensive functional analysis of alphaGs, with a particular focus on alpha 1-giardin, a leading vaccine candidate against giardiasis and a putative virulence-associated UPS substrate. We show that alphaGs localize to peripheral endocytic compartments (PECs), specialized endo-lysosomal organelles implicated in protein release, and engage in multiprotein complexes enriched in UPS substrates. Using lipid-binding assays, cross-linking mass spectrometry, and structural modelling, we define conserved and divergent features of αG membrane interactions. Focusing on α1G, we combine site-directed mutagenesis, confocal microscopy, and mass photometry to demonstrate a critical role for its short N-terminal region in oligomerization and membrane association. Integration of these data with molecular dynamics simulations reveals how alpha1G's oligomeric state and membrane occupancy are regulated at PEC membranes. Together, our findings establish a mechanistic framework for annexin-mediated UPS in Giardia and provide structural insights relevant to parasite virulence and vaccine development. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13, PR00P3_179813, 310030_219372 University of Bern-GHA-SciEx National Research Foundation-Postdoctoral Fellowship

BACKGROUND:Deep mutational scanning (DMS) provides comprehensive maps of protein variant effects but remains experimentally intensive. Machine learning (ML) approaches have the potential to reduce experimental burden of DMS by predicting the functional impact of substitutions from limited data. RESULTS:We introduced a ML classifier trained on normalised DMS scores from SARS-CoV-2 main protease (Mpro) to categorise amino acid substitutions as functional (wild-type-like) or non-functional. Using brute-force feature selection, we identified minimal subsets of six substitution scores per residue that enable accurate classification of the remaining substitutions, achieving minimum (worst accuracy) scores exceeding 90%. Models including support vector machines, random forests, and logistic regression were evaluated without retraining (zero-shot prediction) against additional SARS-CoV-2 Mpro datasets and against unrelated datasets. The zero-shot performance of the models was strongest for other enzymes and more modest when applied to DMS systems that assess protein folding and/or protein-protein interactions. CONCLUSION:The results show that targeted DMS combined with ML can reduce sequencing and reagent costs while preserving classification accuracy, offering a practical route to accelerate variant effect prediction.

Targeting Plasmodium falciparum (Pf) aminoacyl tRNA synthetases is a viable strategy to overcome malaria parasite multi-drug resistance. Here, we focused on Pf Isoleucyl tRNA synthetase (PfIleRS) to identify potential allosteric inhibitors from 1019 South African Natural Compounds (SANC). Eleven potential hits, which passed ADMET and PAINS, were selected based on their docking binding affinity which was higher for PfIleRS than for human IleRS. Molecular dynamics simulations revealed that the compounds, particularly SANC456, commonly induced considerable changes in the global conformation and dynamics of PfIleRS, suggesting potential allosteric modulatory effects. Importantly, all 11 SANC hits reduced the binding affinity of the nucleotide AMP molecule by at least 25%. Some SANC ligand-bound systems (SANC456, SANC1095, and SANC1104) significantly increased the distance between the AMP and Ile ligands. Possible explanations for these changes were explored using three dynamic residue network centrality metrics. Betweenness centrality identified a major allosteric pathway in holo PfIleRS spanning the entire protein length. In contrast, SANC382, SANC456, SANC522, SANC806 and SANC1095 ligand-bound systems exhibited delta BC pathways (SANC-protein minus holo-protein), induced by the ligands, extending from their respective pockets into the active site. Additionally, eigenvector centrality revealed two important residue clusters either side of the holo active site which became altered in the ligand-bound systems, indicating possible allosteric activity. Lastly, many SANC systems showed decreased closeness centrality of zinc finger and active site residues, including the HYGH and KMSKR motifs. We believe that the compounds identified in this study as potential allosteric inhibitors have strong translational potential and warrant further investigation through in vitro and in vivo experiments. Overall, they hold promise as starting points for the development of new and effective antimalarial therapies, particularly against multidrug-resistant Plasmodium parasites.

Understanding the effects of single-nucleotide variations on the three-dimensional structures of drug-metabolizing enzymes at the atomic level is not a well-studied field and deserves attention as it could provide insights into tackling both drug resistance and drug efficacy/toxicity issues. Here, we examined molecular dynamics simulations of the isoniazid-metabolizing enzyme arylamine N-acetyltransferase 2 (NAT2) and established a computational approach to decipher how NAT2 transitions from being a rapid acetylator to a slow acetylator in the presence of residue variations. After revealing slight differences between two rapid acetylators (the NAT2 reference allele and the legacy reference allele), we compared them to five variants with slow clinical phenotypes. In all slow acetylators, we observed a significant reduction in hydrogen bonding and dynamic residue network metric values in functional residues. For example, in the R64Q+K268R variant, the R64Q substitution causes a substantial reduction of hydrogen bonds, resulting in increased root-mean-square fluctuation and changes in residue networks. This is evidenced by the significantly decreased betweenness centrality values of catalytic D122 and putative isoniazid-binding residues S125 and F217, as well as the significantly decreased eigencentrality values of N72, D122, and the active site loop residue G124. Also, in this variant, the average distance between the putative isoniazid-binding residue F217 and catalytic residues H107 and D122 is significantly reduced. This structural shift may affect ligand binding affinity and catalysis. Overall, our findings offer new insights into how allosteric variations alter the NAT2 mechanism, leading to changes in drug exposure.

Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (Mpro). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which Mpro trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for Mpro closely matching those of NSP10, NSP16, and PLpro. The ANN |R| value for Mpro was 0.564, while NSP10, NSP16, and PLpro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for Mpro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PLpro, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from Mpro. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.

Pyrimidine nucleotides are essential for a wide variety of cellular processes and are synthesized either via a salvage pathway or through de novo biosynthesis. The latter is particularly important in proliferating cells, such as infectious diseases and cancer cells. Aspartate transcarbamoylase (ATCase) catalyzes the first committed and rate-limiting step in the de novo pyrimidine biosynthesis pathway, making it an attractive therapeutic target for various diseases. This review summarizes the development of a series of allosteric ATCase inhibitors, advancing them as potential candidates for malarial, tuberculosis and cancer therapies. Furthermore, it explores the potential for these compounds to be expanded into drugs targeting neglected tropical diseases, antimicrobial-resistant infections caused by the ESKAPE pathogens, and their possible application as herbicides. We identify the likely equivalent allosteric pocket in these systems and perform a structure and sequence-based analysis of the residues comprising it, providing a rationale for continued exploration of this compound series as both specific and broad-range inhibitors. The review concludes by emphasizing the importance of continued research into ATCase inhibitors, given their potential broad applicability in treating diverse diseases to enhance both human health and agricultural practices.

There is growing research on the allosteric behaviour of proteins, including studies on allosteric mutations that contribute to human diseases and the development of allosteric drugs. Allostery also plays a key role in drug metabolism, an essential factor in drug development. However, population specific variations, particularly in 3D protein structures, remain understudied. This study focuses on CYP3A4, a key enzyme responsible for metabolizing over 50% of FDA-approved drugs and often linked to adverse drug reactions. Given the vast genetic diversity of Africa, we investigated 13 CYP3A4 alleles from African populations using post-molecular dynamics analyses, with 12 being single variations and one containing a double variation. Except for one, all allele variations were located away from the active site, suggesting allosteric effects. Our comparative analyses of reference and variant structures, through hydrogen bond interactions, dynamic residue network analysis and substrate channel dynamics, revealed notable differences at both global and residue levels. The *32-I335T variant showed the largest changes compared to the reference structure, while *3-M445T (near normal metabolizer) exhibited the least change, with other variants falling in between. The *32-I335T variant showed a distorted conformation in the radius of gyration, a distinct kink in the I helix with specific hydrogen bonds and altered channel patterns. The *12-L373F variant, associated with reduced metabolism of midazolam and quinine, showed increased rigidity in its vicinity, potentially interfering with catalytic activity. Our findings align with clinical and wet lab data, suggesting that our approaches could be applied to analyse variants without clinical evidence.

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

We report the outcomes of the second session of the free online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2022" that took place from 09 to 11 March 2022. The first session was held from 08 to 10 March 2021 and drew the attention of many early career scientists from academia and industry. The 23 invited speakers of this year's workshop also came from academia and industry and 222 registered participants from five continents (Africa, Asia, Europe, South, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites or natural products as drug candidates and drug leads. For three days, the participants of this online workshop discussed modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. These were followed by oral presentations during which much interaction between the speakers and the audience was observed. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) upon submission of an abstract. The final program available on the workshop website (https://indiayouth.info/index.php/caismd) comprised three keynote lectures, 14 oral presentations, two round table discussions, and four hands-on sessions. This meeting report also references internet resources for computational biology around secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community.

BACKGROUND:The use of microsporidia as a disease-transmission-blocking tool has garnered significant attention. Microsporidia sp. MB, known for its ability to block malaria development in mosquitoes, is an optimal candidate for supplementing malaria vector control methods. This symbiont, found in Anopheles mosquitoes, can be transmitted both vertically and horizontally with minimal effects on its mosquito host. Its genome, recently sequenced from An. arabiensis, comprises a compact 5.9 Mbp. RESULTS:Here, we analyze the Microsporidia sp. MB genome, highlighting its major genomic features, gene content, and protein function. The genome contains 2247 genes, predominantly encoding enzymes. Unlike other members of the Enterocytozoonida group, Microsporidia sp. MB has retained most of the genes in the glycolytic pathway. Genes involved in RNA interference (RNAi) were also identified, suggesting a mechanism for host immune suppression. Importantly, meiosis-related genes (MRG) were detected, indicating potential for sexual reproduction in this organism. Comparative analyses revealed similarities with its closest relative, Vittaforma corneae, despite key differences in host interactions. CONCLUSION:This study provides an in-depth analysis of the newly sequenced Microsporidia sp. MB genome, uncovering its unique adaptations for intracellular parasitism, including retention of essential metabolic pathways and RNAi machinery. The identification of MRGs suggests the possibility of sexual reproduction, offering insights into the symbiont's evolutionary strategies. Establishing a reference genome for Microsporidia sp. MB sets the foundation for future studies on its role in malaria transmission dynamics and host-parasite interactions.

We report the draft whole-genome assembly of Microsporidia sp. MB, a symbiotic malaria-transmission-blocking microsporidian isolated from Anopheles arabiensis in Kenya. The whole-genome sequence of Microsporidia sp. MB has a length of 5,908,979 bp, 2,335 contigs, and an average GC content of 31.12%.

Plasmodium falciparum aminoacyl tRNA synthetases (PfaaRSs) are potent antimalarial targets essential for proteome fidelity and overall parasite survival in every stage of the parasite’s life cycle. So far, some of these proteins have been singly targeted yielding inhibitor compounds that have been limited by incidences of resistance which can be overcome via pan-inhibition strategies. Hence, herein, for the first time, we report the identification and in vitro antiplasmodial validation of Mitomycin (MMC) as a probable pan-inhibitor of class 1a (arginyl(A)-, cysteinyl(C), isoleucyl(I)-, leucyl(L), methionyl(M), and valyl(V)-) PfaaRSs which hypothetically may underlie its previously reported activity on the ribosomal RNA to inhibit protein translation and biosynthesis. We combined multiple in silico structure-based discovery strategies that first helped identify functional and druggable sites that were preferentially targeted by the compound in each of the plasmodial proteins: Ins1-Ins2 domain in Pf-ARS; anticodon binding domain in Pf-CRS; CP1-editing domain in Pf-IRS and Pf-MRS; C-terminal domain in Pf-LRS; and CP-core region in Pf-VRS. Molecular dynamics studies further revealed that MMC allosterically induced changes in the global structures of each protein. Likewise, prominent structural perturbations were caused by the compound across the functional domains of the proteins. More so, MMC induced systematic alterations in the binding of the catalytic nucleotide and amino acid substrates which culminated in the loss of key interactions with key active site residues and ultimate reduction in the nucleotide-binding affinities across all proteins, as deduced from the binding energy calculations. These altogether confirmed that MMC uniformly disrupted the structure of the target proteins and essential substrates. Further, MMC demonstrated IC50 < 5μM against the Dd2 and 3D7 strains of parasite making it a good starting point for malarial drug development. We believe that findings from our study will be important in the current search for highly effective multi-stage antimalarial drugs.

Microsporidia are spore-forming eukaryotes that are related to fungi but have unique traits that set them apart. They have compact genomes as a result of evolutionary gene loss associated with their complete dependency on hosts for survival. Despite having a relatively small number of genes, a disproportionately high percentage of the genes in microsporidia genomes code for proteins whose functions remain unknown (hypothetical proteins-HPs). Computational annotation of HPs has become a more efficient and cost-effective alternative to experimental investigation. This research developed a robust bioinformatics annotation pipeline of HPs from Vittaforma corneae, a clinically important microsporidian that causes ocular infections in immunocompromised individuals. Here, we describe various steps to retrieve sequences and homologs and to carry out physicochemical characterization, protein family classification, identification of motifs and domains, protein-protein interaction network analysis, and homology modelling using a variety of online resources. Classification of protein families produced consistent findings across platforms, demonstrating the accuracy of annotation utilizing in silico methods. A total of 162 out of 2034 HPs were fully annotated, with the bulk of them categorized as binding proteins, enzymes, or regulatory proteins. The protein functions of several HPs from Vittaforma corneae were accurately inferred. This improved our understanding of microsporidian HPs despite challenges related to the obligate nature of microsporidia, the absence of fully characterized genes, and the lack of homologous genes in other systems.

Carbonic anhydrases (CAs) are metalloenzymes that can help organisms survive in hydrothermal vents by hydrating carbon dioxide (CO2). In this study, we focus on alpha (α), beta (β), and gamma (γ) CAs, which are present in the thermophilic microbiome of marine hydrothermal vents. The coding genes of these enzymes can be transferred between hydrothermal-vent organisms via horizontal gene transfer (HGT), which is an important tool in natural biodiversity. We performed big data mining and bioinformatics studies on α-, β-, and γ-CA coding genes from the thermophilic microbiome of marine hydrothermal vents. The results showed a reasonable association between thermostable α-, β-, and γ-CAs in the microbial population of the hydrothermal vents. This relationship could be due to HGT. We found evidence of HGT of α- and β-CAs between Cycloclasticus sp., a symbiont of Bathymodiolus heckerae, and an endosymbiont of Riftia pachyptila via Integrons. Conversely, HGT of β-CA genes from the endosymbiont Tevnia jerichonana to the endosymbiont Riftia pachyptila was detected. In addition, Hydrogenovibrio crunogenus SP-41 contains a β-CA gene on genomic islands (GIs). This gene can be transferred by HGT to Hydrogenovibrio sp. MA2-6, a methanotrophic endosymbiont of Bathymodiolus azoricus, and a methanotrophic endosymbiont of Bathymodiolus puteoserpentis. The endosymbiont of R. pachyptila has a γ-CA gene in the genome. If α- and β-CA coding genes have been derived from other microorganisms, such as endosymbionts of T. jerichonana and Cycloclasticus sp. as the endosymbiont of B. heckerae, through HGT, the theory of the necessity of thermostable CA enzymes for survival in the extreme ecosystem of hydrothermal vents is suggested and helps the conservation of microbiome natural diversity in hydrothermal vents. These harsh ecosystems, with their integral players, such as HGT and endosymbionts, significantly impact the enrichment of life on Earth and the carbon cycle in the ocean.

Enteroviruses are one of the most abundant groups of viruses infecting humans, and yet there are no approved antivirals against them. To find effective antiviral compounds against enterovirus B group viruses, an in-house chemical library was screened. The most effective compounds against Coxsackieviruses B3 (CVB3) and A9 (CVA9) were CL212 and CL213, two N-phenyl benzamides. Both compounds were more effective against CVA9 and CL213 gave a better EC50 value of 1 µM with high a specificity index of 140. Both drugs were most effective when incubated directly with viruses suggesting that they mainly bound to the virions. A real-time uncoating assay showed that the compounds stabilized the virions and radioactive sucrose gradient as well as TEM confirmed that the viruses stayed intact. A docking assay, taking into account larger areas around the 2-and 3-fold axes of CVA9 and CVB3, suggested that the hydrophobic pocket gives the strongest binding to CVA9 but revealed another binding site around the 3-fold axis which could contribute to the binding of the compounds. Together, our data support a direct antiviral mechanism against the virus capsid and suggest that the compounds bind to the hydrophobic pocket and 3-fold axis area resulting in the stabilization of the virion.

Precision medicine gives individuals tailored medical treatment, with the genotype determining the therapeutic strategy, the appropriate dosage, and the likelihood of benefit or toxicity. Cytochrome P450 (CYP) enzyme families 1, 2, and 3 play a pivotal role in eliminating most drugs. Factors that affect CYP function and expression have a major impact on treatment outcomes. Therefore, polymorphisms of these enzymes result in alleles with diverse enzymatic activity and drug metabolism phenotypes. Africa has the highest CYP genetic diversity and also the highest burden of malaria and tuberculosis, and this review presents current general information on CYP enzymes together with variation data concerning antimalarial and antituberculosis drugs, while focusing on the first three CYP families. Afrocentric alleles such as CYP2A6*17, CYP2A6*23, CYP2A6*25, CYP2A6*28, CYP2B6*6, CYP2B6*18, CYP2C8*2, CYP2C9*5, CYP2C9*8, CYP2C9*9, CYP2C19*9, CYP2C19*13, CYP2C19*15, CYP2D6*2, CYP2D6*17, CYP2D6*29, and CYP3A4*15 are implicated in diverse metabolic phenotypes of different antimalarials such as artesunate, mefloquine, quinine, primaquine, and chloroquine. Moreover, CYP3A4, CYP1A1, CYP2C8, CYP2C18, CYP2C19, CYP2J2, and CYP1B1 are implicated in the metabolism of some second-line antituberculosis drugs such as bedaquiline and linezolid. Drug-drug interactions, induction/inhibition, and enzyme polymorphisms that influence the metabolism of antituberculosis, antimalarial, and other drugs, are explored. Moreover, a mapping of Afrocentric missense mutations to CYP structures and a documentation of their known effects provided structural insights, as understanding the mechanism of action of these enzymes and how the different alleles influence enzyme function is invaluable to the advancement of precision medicine.

The use of computer-aided methods have continued to propel accelerated drug discovery across various disease models, interestingly allowing the specific inhibition of pathogenic targets. Chloride Intracellular Channel Protein 4 (CLIC4) is a novel class of intracellular ion channel highly implicated in tumor and vascular biology. It regulates cell proliferation, apoptosis and angiogenesis; and is involved in multiple pathologic signaling pathways. Absence of specific inhibitors however impedes its advancement to translational research. Here, we integrate structural bioinformatics and experimental research approaches for the discovery and validation of small-molecule inhibitors of CLIC4. High-affinity allosteric binders were identified from a library of 1615 Food and Drug Administration (FDA)-approved drugs via a high-performance computing-powered blind-docking approach, resulting in the selection of amphotericin B and rapamycin. NMR assays confirmed the binding and conformational disruptive effects of both drugs while they also reversed stress-induced membrane translocation of CLIC4 and inhibited endothelial cell migration. Structural and dynamics simulation studies further revealed that the inhibitory mechanisms of these compounds were hinged on the allosteric modulation of the catalytic glutathione (GSH)-like site loop and the extended catalytic β loop which may elicit interference with the catalytic activities of CLIC4. Structure-based insights from this study provide the basis for the selective targeting of CLIC4 to treat the associated pathologies.

Emerging Mycobacterium tuberculosis (Mtb) resistant strains have continued to limit the efficacies of existing antitubercular therapies. More specifically, mutations in the RNA replicative machinery of Mtb, RNA polymerase (RNAP), have been widely linked to rifampicin (RIF) resistance, which has led to therapeutic failures in many clinical cases. Moreover, elusive details on the underlying mechanisms of RIF-resistance caused by Mtb-RNAP mutations have hampered the development of new and efficient drugs that are able to overcome this challenge. Therefore, in this study we attempt to resolve the molecular and structural events associated with RIF-resistance in nine clinically reported missense Mtb RNAP mutations. Our study, for the first time, investigated the multi-subunit Mtb RNAP complex and findings revealed that the mutations commonly disrupted structural–dynamical attributes that may be essential for the protein’s catalytic functions, particularly at the βfork loop 2, β’zinc-binding domain, the β’ trigger loop and β’jaw, which in line with previous experimental reports, are essential for RNAP processivity. Complementarily, the mutations considerably perturbed the RIF-BP, which led to alterations in the active orientation of RIF needed to obstruct RNA extension. Consequentially, essential interactions with RIF were lost due to the mutation-induced repositioning with corresponding reductions in the binding affinity of the drug observed in majority of the mutants. We believe these findings will significantly aid future efforts in the discovery of new treatment options with the potential to overcome antitubercular resistance.

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.