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.

L'Organisation mondiale de la Santé (OMS) vise à réduire le nombre de nouveaux cas de lèpre de 70% d'ici 2030, ce qui nécessite un meilleur diagnostic de la maladie. Dans le présent document, nous évoquons le développement de deux profils de produit cible établis par l'OMS à cette fin. Ces profils définissent des critères en matière d'utilisation, de conception, de performances, de configuration et de distribution du produit, en accordant une attention particulière à l'accessibilité et à l'abordabilité. Le premier profil de produit cible décrit les exigences pour les tests servant à confirmer le diagnostic de la lèpre chez les individus qui présentent des signes cliniques et des symptômes, afin d'orienter l'instauration d'un traitement à base de plusieurs médicaments. Le second profil de produit cible décrit les exigences pour les tests servant à détecter une infection à Mycobacterium leprae ou M. lepromatosis parmi les contacts asymptomatiques de patients lépreux, ce qui contribue à l'adoption de mesures prophylactiques et à la prévention. Nous avons eu recours à une modélisation statistique pour évaluer les exigences de sensibilité et de spécificité de ces tests diagnostiques. Cet article met en évidence les obstacles à l'atteinte d'un niveau élevé de spécificité en raison de l'endémicité variable de M. leprae, et à l'identification d'analytes cibles offrant de bons résultats chez les phénotypes lépreux. Nous concluons qu'un diagnostic reposant sur des caractéristiques de performance et de conception appropriées est essentiel pour détecter rapidement la maladie et intervenir en amont, et nous plaidons pour une prévention plutôt qu'une gestion de la lèpre.

La Organización Mundial de la Salud (OMS) pretende reducir los nuevos casos de lepra en un 70% para 2030, lo que requiere avances en el diagnóstico de la lepra. Aquí se analiza el desarrollo de dos perfiles de productos objetivo de la OMS para este tipo de diagnósticos. Estos perfiles definen los criterios de uso, diseño, rendimiento, configuración y distribución de los productos, centrándose en su accesibilidad y asequibilidad. El primer perfil de producto objetivo describe los requisitos de las pruebas para confirmar el diagnóstico de la lepra en personas con signos y síntomas clínicos, con el fin de orientar el inicio del tratamiento con múltiples fármacos. El segundo perfil de producto objetivo describe los requisitos de las pruebas para detectar la infección por Mycobacterium leprae o M. lepromatosis entre los contactos asintomáticos de los pacientes con lepra, para facilitar las intervenciones profilácticas y la prevención. Se utilizaron modelos estadísticos para evaluar los requisitos de sensibilidad y especificidad de estas pruebas diagnósticas. El artículo destaca las dificultades para lograr una alta especificidad, dada la diferente endemicidad de M. leprae, y para identificar analitos diana con un rendimiento sólido en todos los fenotipos de lepra. Concluimos que los diagnósticos con un diseño de producto y unas características de rendimiento adecuados son fundamentales para la detección precoz y la intervención preventiva, lo que favorece la transición del manejo de la lepra a la prevención.

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.

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.

A combined effort of 11 laboratories in the WHO African region to improve quality of Buruli ulcer PCR diagnosis: The "BU-LABNET".

Buruli ulcer is one of the 20 neglected tropical diseases in the world. This necrotizing hypodermitis is a chronic debilitating disease caused by an environmental Mycobacterium ulcerans. At least 33 countries with tropical, subtropical and temperate climates have reported Buruli ulcer in African countries, South America and Western Pacific regions. Majority of cases are spread across West and Central Africa. The mode of transmission is unclear, hindering the implementation of adequate prevention for the population. Currently, early diagnosis and treatment are crucial to minimizing morbidity, costs and preventing long-term disability. Biological confirmation of clinical diagnosis of Buruli ulcer is essential before starting chemotherapy. Indeed, differential diagnosis are numerous and Buruli ulcer has varying clinical presentations. Up to now, the gold standard biological confirmation is the quantitative PCR, targeting the insertion sequence IS2404 of M. ulcerans performed on cutaneous samples. Due to the low PCR confirmation rate in endemic African countries (under 30% in 2018) for numerous identified reasons within this article, 11 laboratories decided to combine their efforts to create the network "BU-LABNET" in 2019. The first step of the network was to harmonize the procedures and ship specific reagents to each laboratory. With this system in place, implementation of these procedures for testing and follow-up was easy and the laboratories were able to carry out their first quality control with a very high success rate. It is now time to integrate other neglected tropical diseases to this platform, such as yaws or 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.

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

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.

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.

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.

Strategies for drug target identification in Mycobacterium leprae.

Hansen's disease (HD), or leprosy, continues to be endemic in many parts of the world. Although multidrug therapy (MDT) is successful in curing a large number of patients, some of them abandon it because it is a long-term treatment. Therefore, identification of new drug targets in Mycobacterium leprae is considered of high importance. Here, we introduce an overview of in silico and in vitro studies that might be of help in this endeavor. The essentiality of M. leprae proteins is reviewed with discussion of flux balance analysis, gene expression, and knockout articles. Finally, druggability techniques are proposed for the validation of new M. leprae protein targets (see Fig. 1).

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.

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae.

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

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.

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/ .

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.

Molecular epidemiology and transmission dynamics of leprosy among multicase families and case-contact pairs.

BACKGROUND: Human-to-human transmission of Mycobacterium leprae among household contacts of active leprosy cases is significant, and surveillance of household contacts is vital to interrupting the transmission chain for this disease. This study was conducted to identify similarities in M. leprae strains, based on genomic single nucleotide polymorphisms (SNPs), among cases and their household contacts and in multicase families in order to decipher possible associations, transmission links, various clinical conditions of index cases that enhance person-to-person transmission, and timelines for transmission patterns. METHODS: PCR for M. leprae DNA detection (amplification of the Rlep gene) and SNP subtyping of M. leprae strains was performed for 61 index cases and one of their household contacts. Additionally, we studied six families with multiple cases of leprosy, to understand timelines of infectivity and its relation to severity of the disease in the index cases. RESULTS: Index cases with lepromatous (LL) and borderline lepromatous (BL) leprosy, together with a positive bacteriological index (BI) for M. leprae, result in a higher percentage of their contacts subclinically infected with M. leprae, with odds ratios (OR) of 6.6 (95% confidence interval (CI) 1.6-27.6) for BL and LL, and 7.07 (CI 1.41-35.41) for BI-positive index cases. 75% of the case-contact pairs had a similar SNP subtype of M. leprae. The timeline of infection in multicase families revealed that contacts were infected during the BI-positive period of the index case. CONCLUSION: Using molecular methods, we determined that positivity for M. leprae DNA in contacts of index leprosy cases was attributed to clinical characteristics of leprosy in the index cases. LL and BL forms of leprosy, together with positive BI, contributed to dissemination of infection to household contacts. In conclusion, we found a relationship between SNP subtypes within index case-contact pairs. This method can help decipher the transmission patterns and identify individuals at risk of contracting leprosy.

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 ß-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 ß-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 ß-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 ß-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 ß-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 PDE6αß 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 PDE6αß 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 PDE6αß 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.

Molecular docking and simulation study for synthesis of alternative dapsone derivative as a newer antileprosy drug in multidrug therapy.

Leprosy (causative, Mycobacterium leprae) continues to be the persisting public health problem with stable incidence rates, owing to the emergence of dapsone resistance that being the principal drug in the ongoing multidrug therapy. Hence, to overcome the drug resistance, structural modification through medicinal chemistry was used to design newer dapsone derivative(s) (DDs), against folic acid biosynthesis pathway. The approach included theoretical modeling, molecular docking, and molecular dynamic (MD) simulation as well as binding free energy estimation for validation of newly designed seven DDs, before synthesis. Theoretical modeling, docking, and MD simulation studies were used to understand the mode of binding and efficacy of DDs against the wild-type and mutant dihydropteroate synthases (DHPS). Principal component analysis was performed to understand the conformational dynamics of DHPS-DD complexes. Furthermore, the overall stability and negative-binding free energy of DHPS-DD complexes were deciphered using Molecular Mechanics/Poisson-Boltzmann Surface Area technique. Molecular mechanics study revealed that DD3 possesses higher binding free energy than dapsone against mutant DHPS. Energetic contribution analysis portrayed that van der Waals and electrostatic energy contributes profoundly to the overall negative free energy, whereas polar solvation energy opposes the binding. Finally, DD3 was synthesized and characterized using Fourier-transform infrared spectroscopy, UV, liquid chromatography-mass spectrometry, and proton nuclear magnetic resonance techniques. This study suggested that DD3 could be further promoted as newer antileprosy agent. The principles of medicinal chemistry and bioinformatics tools help to locate effective therapeutics to minimize resources and time in current drug development modules.

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

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