Target product profiles: leprosy diagnostics

The World Health Organization (WHO) aims to reduce new leprosy cases by 70% by 2030, necessitating advancements in leprosy diagnostics. Here we discuss the development of two WHO's target product profiles for such diagnostics. These profiles define criteria for product use, design, performance, configuration and distribution, with a focus on accessibility and affordability. The first target product profile outlines requirements for tests to confirm diagnosis of leprosy in individuals with clinical signs and symptoms, to guide multidrug treatment initiation. The second target product profile outlines requirements for tests to detect Mycobacterium leprae or M. lepromatosis infection among asymptomatic contacts of leprosy patients, aiding prophylactic interventions and prevention. Statistical modelling was used to assess sensitivity and specificity requirements for these diagnostic tests. The paper highlights challenges in achieving high specificity, given the varying endemicity of M. leprae, and identifying target analytes with robust performance across leprosy phenotypes. We conclude that diagnostics with appropriate product design and performance characteristics are crucial for early detection and preventive intervention, advocating for the transition from leprosy management to prevention.

Structure-guided computational insecticide discovery targeting β-N-acetyl-D-hexosaminidase of Ostrinia furnacalis

Ostrinia furnacalis is a species of moth in the Crambidae family that is harmful to maize and other corn crops in Southeast Asia and the Western Pacific regions. Ostrinia furnacalis causes devastating losses to economically important corn fields. The β-N-acetyl-D-hexosaminidase is an essential enzyme in O. furnacalis and its substrate binding +1 active site is different from that of the plants and humans β-N-acetyl-D-hexosaminidases. To develop environment-friendly insecticides against OfHex1, we conducted structure-guided computational insecticide discovery to identify potential inhibitors that can bind the active site and inhibit the substrate binding and activity of the enzyme. We adopted a three-pronged strategy to conduct virtual screening using Glide and virtual screening workflow (VSW) in Schrödinger Suite-2022-3, against crystal structures of OfHex1 (PDB Id:3NSN), its homologue in humans (PDB Id: 1NP0) and Alphafold model of β-N-acetyl-D-hexosaminidase from Trichogramma pretiosum, an egg parasitoid that protects the crops from O. furnacalis. A library of 20,313 commercially available and "insecticide-like" compounds was extracted from published literature. LigPrep enabled 44,943 ready-to-dock conformers generation. Glide docking revealed 18 OfHex1-specific hits that were absent in human and T. pretiosum screens. Reference docking was conducted using inhibitors/natural ligands in the crystal structures and hits with better docking scores than the reference were selected for MD simulations using Desmond to understand the stability of hit-target interactions. We noted five compounds that bound to OfHex1 TMX active-site based on their docking scores, consistent binding as noted by MD simulations and their insecticide/pesticide likeliness as noted by the Comprehensive Pesticide Likeness Analysis.Communicated by Ramaswamy H. Sarma.

Identification of selective inhibitors for phosphodiesterase 5A using e-pharmacophore modelling and large-scale virtual screening-based structure guided drug discovery approaches

The inhibition of Phosphodiesterase 5A (PDEA5) has the potential to modulate pulmonary arterial hypertension and cardiovascular diseases. Exploring the cross-reactivity of clinically available PDE5A therapeutics with PDE6A is intriguing in order to develop highly selective PDE5A compounds in cardiovascular arena. In the current study, we leveraged e-pharmacophore based screening and molecular dynamics (MD) simulation to discover more selective PDE5A inhibitors as compared to the PDE6A catalytic domain. e-Pharmacophore based mapping of the CoCoCo database (7 million compounds: ∼ 150,000,000 conformers), followed by Glide docking, MM-GBSA, and protein-inhibitor interaction analysis, revealed 1536427, 4832637 and 6788240 as stable, tight binders of PDE5A instead of PDE6A. These compounds adhere to Lipinski Rule of Five (RO5) and ADME/Tox criteria. MD simulations analysis showed that 1536427 stays stable and tightly binds to catalytic (Q-region) core of PDE5A catalytic domain as compared to sildenafil. Pronounced inward motions of the hydrophobic (H-region) and Lid region indicate the closure of PDE5A-1536427 complex, whereas this region in PDE6A-1536427 is more open. Significant differences in the interactions, stability, and dynamics of 1536427 were observed in the catalytic domain of PDE6A, demonstrating less specificity for PDE6A in comparison to PDE5A. After lead optimization and therapeutic interventions, this proposed lead may emerge as a promising PDE5A selective inhibitor.Communicated by Ramaswamy H. Sarma.

Multi-centric evaluation of Biomeme Franklin Mobile qPCR for rapid detection of Mycobacterium ulcerans in clinical specimens

The gold standard for detection of Mycobacterium ulcerans is PCR due to its high accuracy in confirmation of suspected cases. But the available PCR assays are designed for standard size thermocyclers which are immobile and suited for reference laboratories often located long distances from endemic communities. This makes it a challenge to obtain immediate results for patient management. We validated and evaluated a dried reagent-based PCR assay adapted for a handheld, battery-operated, portable thermocycler with the potential to extend diagnostics to endemic communities with limited infrastructure. The diagnostic accuracy of the assay following a multi-center evaluation by three Buruli ulcer reference laboratories with over 300 clinical samples showed sensitivity and specificity of 100-97% and 100-94%, respectively using centralized IS2404 quantitative PCR platform as a reference standard. This assay coupled with a field-friendly extraction method fulfill almost all the target product profiles of Buruli ulcer for decentralized testing at the district, health center and community levels; a key critical action for achieving the NTD Road Map 2030 target for Buruli ulcer.

SARS-CoV-2 Omicron subvariant spike N405 unlikely to rapidly deamidate

The RGD motif on the SARS-CoV-2 spike protein has been suggested to interact with RGD-binding integrins αVβ3 and α5β1 to enhance viral cell entry and alter downstream signaling cascades. The D405N mutation on the Omicron subvariant spike proteins, resulting in an RGN motif, has recently been shown to inhibit binding to integrin αVβ3. Deamidation of asparagines in protein ligand RGN motifs has been demonstrated to generate RGD and RGisoD motifs that permit binding to RGD-binding integrins. Two asparagines, N481 and N501, on the Wild-type spike receptor-binding domain have been previously shown to have deamidation half-lives of 16.5 and 123 days, respectively, which may occur during the viral life cycle. Deamidation of Omicron subvariant N405 may recover the ability to interact with RGD-binding integrins. Thus, herein, all-atom molecular dynamics simulations of the Wild-type and Omicron subvariant spike protein receptor-binding domains were conducted to investigate the potential for asparagines, the Omicron subvariant N405 in particular, to assume the optimized geometry for deamidation to occur. In summary, the Omicron subvariant N405 was primarily found to be stabilized in a state unfavourable for deamidation after hydrogen bonding with downstream E406. Nevertheless, a small number of RGD or RGisoD motifs on the Omicron subvariant spike proteins may restore the ability to interact with RGD-binding integrins. The simulations also provided structural clarification regarding the deamidation rates of Wild-type N481 and N501 and highlighted the utility of tertiary structure dynamics information in predicting asparagine deamidation. Further work is needed to characterize the effects of deamidation on spike-integrin interactions.

Mycobacterium leprae and host immune transcriptomic signatures for reactional states in leprosy

Background

Mycobacterium leprae transcriptomic and human host immune gene expression signatures that demonstrate a plausible association with type I (T1R) and type II reactions (T2R) aid in early diagnosis, prevention of nerve damage and consequent demyelinating neuropathy in leprosy. The aim of the study is to identify M. leprae and host-associated gene-expression signatures that are associated with reactional states in leprosy.

Methods

The differentially expressed genes from the whole transcriptome of M. leprae were determined using genome-wide hybridization arrays with RNA extracted from skin biopsies of 20 T1R, 20 T2R and 20 non reactional controls (NR). Additionally, human immune gene-expressions were profiled using RT2-PCR profiler arrays and real-time qPCRs.

Results

The RNA quality was optimal in 16 NR, 18 T1R and 19 T2R samples. Whole transcriptome expression array of these samples revealed significant upregulation of the genes that encode integral and intrinsic membrane proteins, hydrolases and oxidoreductases. In T1R lesional skin biopsy specimens, the top 10 significantly upregulated genes are ML2064, ML1271, ML1960, ML1220, ML2498, ML1996, ML2388, ML0429, ML2030 and ML0224 in comparison to NR. In T2R, genes ML2498, ML1526, ML0394, ML1960, ML2388, ML0429, ML0281, ML1847, ML1618 and ML1271 were significantly upregulated. We noted ML2664 was significantly upregulated in T1R and repressed in T2R. Conversely, we have not noted any genes upregulated in T2R and repressed in T1R. In both T1R and T2R, ML2388 was significantly upregulated. This gene encodes a probable membrane protein and epitope prediction using Bepipred-2.0 revealed a distinct B-cell epitope. Overexpression of ML2388 was noted consistently across the reaction samples. From the host immune gene expression profiles, genes for CXCL9, CXCL10, CXCL2, CD40LG, IL17A and CXCL11 were upregulated in T1R when compared to the NR. In T2R, CXCL10, CXCL11, CXCL9, CXCL2 and CD40LG were upregulated when compared to the NR group.

Conclusion

A gene set signature involving bacterial genes ML2388, ML2664, and host immune genes CXCL10 and IL-17A can be transcriptomic markers for reactional states in leprosy.

In silico analysis of mutations near S1/S2 cleavage site in SARS-CoV-2 spike protein reveals increased propensity of glycosylation in Omicron strain

Cleavage of the severe respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein has been demonstrated to contribute to viral-cell fusion and syncytia formation. Studies have shown that variants of concern (VOC) and variants of interest (VOI) show differing membrane fusion capacity. Mutations near cleavage motifs, such as the S1/S2 and S2' sites, may alter interactions with host proteases and, thus, the potential for fusion. The biochemical basis for the differences in interactions with host proteases for the VOC/VOI spike proteins has not yet been explored. Using sequence and structure-based bioinformatics, mutations near the VOC/VOI spike protein cleavage sites were inspected for their structural effects. All mutations found at the S1/S2 sites were predicted to increase affinity to the furin protease but not TMPRSS2. Mutations at the spike residue P681 in several strains, such P681R in the Delta strain, resulted in the disruption of a proline-directed kinase phosphorylation motif at the S1/S2 site, which may lessen the impact of phosphorylation for these variants. However, the unique N679K mutation in the Omicron strain was found to increase the propensity for O-linked glycosylation at the S1/S2 cleavage site, which may prevent recognition by proteases. Such glycosylation in the Omicron strain may hinder entry at the cell surface and, thus, decrease syncytia formation and induce cell entry through the endocytic pathway as has been shown in previous studies. Further experimental work is needed to confirm the effect of mutations and posttranslational modifications on SARS-CoV-2 spike protein cleavage sites.

COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets

Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.

Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses

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Potential coronavirus spike protein mimicry revealed by structural comparison.

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Human and non-human protein potential interactions with virus identified.

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Predicted structural mimicry corroborated by protein–protein docking.

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Epitope-based alignments may help guide vaccine efforts.

Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism and druggability of the virus. Peptide and epitope motifs have been detected on coronavirus spike proteins using sequence homology approaches; however, comparing the three-dimensional shape of the protein has been shown as more informative in predicting mimicry than sequence-based comparisons. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known protein models with the receptor-binding motifs and verify potential mimicked interactions with protein docking simulations. Both human and non-human proteins were returned for all three receptor-binding motifs. For example, all three were similar to several proteins containing EGF-like domains: some of which are endogenous to humans, such as thrombomodulin, and others exogenous, such as Plasmodium falciparum MSP-1. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterized them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein-ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are implemented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at https://sars3d.com/. This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance

Tuberculosis (TB) and leprosy are mycobacterial infections caused by Mycobacterium tuberculosis and Mycobacterium leprae respectively. These diseases continue to be endemic in developing countries where the cost of new medicines presents major challenges. The situation is further exacerbated by the emergence of resistance to many front-line antibiotics. A priority now is to design new antimycobacterials that are not only effective in combatting the diseases but are also less likely to give rise to resistance. In both these respects understanding the structure of drug targets in M. tuberculosis and M. leprae is crucial. In this review we describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Exploring the structural basis of conformational heterogeneity and autoinhibition of human cGMP-specific protein kinase Iα through computational modelling and molecular dynamics simulations

Protein kinase Iα (PKGIα) is a pivotal cyclic guanosine monophosphate (cGMP) signalling protein. Major steps related to the structural plasticity of PKGIα have been inferred but the structural aspects of the auto-inhibition and multidomain tertiary organization of human PKGIα in active and inactive form are not clear. Here we combine computational comparative modelling, protein-protein docking and molecular dynamics (MD) simulations to investigate structural details of the repressed state of the catalytic domain of PKGIα. Exploration of the potential inhibitory conformation of the auto-inhibitory domain (AI) within the catalytic cleft reveals that the pseudo-substrate motif binds with residues of the glycine rich loop and substrate-binding lobe. Dynamic changes as a result of coupling of the catalytic and AI domains are also investigated. The three-dimensional homodimeric models of PKGIα in the active and inactive state indicate that PKGIα in its inactive-state attains a compact globular structure where cyclic nucleotide binding (CNB-A/B) domains are buried, whereas the catalytic domains are inaccessible with their substrate-binding pockets facing the N-terminal of CNB-A. Contrary to this, the active-state model of PKGIα shows an extended conformation where CNB-A/B domains are slightly rearranged and the catalytic domains of homodimer flanking the C-terminal with their substrate binding lobes free to entrap downstream proteins. These findings are consistent with previously reported static images of the multidomain organization of PKGIα. Structural insights pertaining to the conformational heterogeneity and auto-inhibition of PKGIα provided in this study may help to understand the dynamics-driven effective regulation of PKGIα.

Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae

Rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic tools. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations in the drug targets. The functional consequences of missense mutations in the β-subunit of RNA polymerase (RNAP) in Mycobacterium leprae (M. leprae) contribute to phenotypic resistance to rifampin in leprosy. Here, we report in-silico saturation mutagenesis of all residues in the β-subunit of RNAP to all other 19 amino acid types (generating 21,394 mutations for 1126 residues) and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on β-subunit-RNA and rifampin affinities (only for the rifampin binding site) using state-of-the-art structure, sequence and normal mode analysis-based methods. Mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and a concomitant decrease in residue-depth (the extent to which a residue is buried in the protein structure space) of the mutant residues. The mutations at residue positions S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall protein stability, and nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the clinically/experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations that destabilise RNAP-rifampin interactions and confer rifampin resistance in leprosy.

Author summary

The emergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses a threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic resistance to rifampin in leprosy, diagnosis mainly relies on the presence of mutations in drug resistance determining regions of the rpoB gene that encodes the β-subunit of RNAP in M. leprae. Few labs in the world perform mouse food pad propagation of M. leprae in the presence of drugs (rifampin) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinically detected drug resistance in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or that can arise in the β-subunit of RNAP in M. leprae. Most of the predicted mutations may not occur in M. leprae due to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a potential choice for small molecule binding and structure guided drug discovery.

The Molecular Organization of Human cGMP Specific Phosphodiesterase 6 (PDE6): Structural Implications of Somatic Mutations in Cancer and Retinitis Pigmentosa

In the cyclic guanosine monophosphate (cGMP) signaling pathway, phosphodiesterase 6 (PDE6) maintains a critical balance of the intracellular concentration of cGMP by catalyzing it to 5′ guanosine monophosphate (5′-GMP). To gain insight into the mechanistic impacts of the PDE6 somatic mutations that are implicated in cancer and retinitis pigmentosa, we first defined the structure and organization of the human PDE6 heterodimer using computational comparative modelling. Each subunit of PDE6αβ possesses three domains connected through long α-helices. The heterodimer model indicates that the two chains are likely related by a pseudo two-fold axis. The N-terminal region of each subunit is comprised of two allosteric cGMP-binding domains (Gaf-A & Gaf-B), oriented in the same way and interacting with the catalytic domain present at the C-terminal in a way that would allow the allosteric cGMP-binding domains to influence catalytic activity. Subsequently, we applied an integrated knowledge-driven in silico mutation analysis approach to understand the structural and functional implications of experimentally identified mutations that cause various cancers and retinitis pigmentosa, as well as computational saturation mutagenesis of the dimer interface and cGMP-binding residues of both Gaf-A, and the catalytic domains. We studied the impact of mutations on the stability of PDE6αβ structure, subunit-interfaces and Gaf-cGMP interactions. Further, we discussed the changes in interatomic interactions of mutations that are destabilizing in Gaf-A (R93L, V141 M, F162 L), catalytic domain (D600N, F742 L, F776 L) and at the dimer interface (F426A, F248G, F424 N). This study establishes a possible link of change in PDE6αβ structural stability to the experimentally observed disease phenotypes.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

Publisher Correction: Structural Implications of Mutations Conferring Rifampin Resistance in Mycobacterium leprae

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Structural Implications of Mutations Conferring Rifampin Resistance in Mycobacterium leprae

The rpoB gene encodes the β subunit of RNA polymerase holoenzyme in Mycobacterium leprae (M. leprae). Missense mutations in the rpoB gene were identified as etiological factors for rifampin resistance in leprosy. In the present study, we identified mutations corresponding to rifampin resistance in relapsed leprosy cases from three hospitals in southern India which treat leprosy patients. DNA was extracted from skin biopsies of 35 relapse/multidrug therapy non-respondent leprosy cases, and PCR was performed to amplify the 276 bp rifampin resistance-determining region of the rpoB gene. PCR products were sequenced, and mutations were identified in four out of the 35 cases at codon positions D441Y, D441V, S437L and H476R. The structural and functional effects of these mutations were assessed in the context of three-dimensional comparative models of wild-type and mutant M. leprae RNA polymerase holoenzyme (RNAP), based on the recently solved crystal structures of RNAP of Mycobacterium tuberculosis, containing a synthetic nucleic acid scaffold and rifampin. The resistance mutations were observed to alter the hydrogen-bonding and hydrophobic interactions of rifampin and the 5' ribonucleotide of the growing RNA transcript. This study demonstrates that rifampin-resistant strains of M. leprae among leprosy patients in southern India are likely to arise from mutations that affect the drug-binding site and stability of RNAP.