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

Computational Deorphaning of <em>Mycobacterium tuberculosis</em> Targets

Tuberculosis (TB) continues to be a major health hazard worldwide due to the resurgence of drug discovery strains of Mycobacterium tuberculosis (Mtb) and co-infection. For decades drug discovery has concentrated on identifying ligands for ~10 Mtb targets, hence most of the identified essential proteins are not utilised in TB chemotherapy. Here computational techniques were used to identify ligands for the orphan Mtb proteins. These range from ligand-based and structure-based virtual screening modelling the proteome of the bacterium. Identification of ligands for most of the Mtb proteins will provide novel TB drugs and targets and hence address drug resistance, toxicity and the duration of TB treatment

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

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

Funder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265Funder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925Abstract: 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/

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

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

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper

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

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

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).American Leprosy Mission Grant code: X5:2177

Sequence homology and expression profile of genes associated with DNA repair pathways in Mycobacterium leprae.

BACKGROUND: Survival of Mycobacterium leprae, the causative bacteria for leprosy, in the human host is dependent to an extent on the ways in which its genome integrity is retained. DNA repair mechanisms protect bacterial DNA from damage induced by various stress factors. The current study is aimed at understanding the sequence and functional annotation of DNA repair genes in M. leprae. METHODS: T he genome of M. leprae was annotated using sequence alignment tools to identify DNA repair genes that have homologs in Mycobacterium tuberculosis and Escherichia coli. A set of 96 genes known to be involved in DNA repair mechanisms in E. coli and Mycobacteriaceae were chosen as a reference. Among these, 61 were identified in M. leprae based on sequence similarity and domain architecture. The 61 were classified into 36 characterized gene products (59%), 11 hypothetical proteins (18%), and 14 pseudogenes (23%). All these genes have homologs in M. tuberculosis and 49 (80.32%) in E. coli. A set of 12 genes which are absent in E. coli were present in M. leprae and in Mycobacteriaceae. These 61 genes were further investigated for their expression profiles in the whole transcriptome microarray data of M. leprae which was obtained from the signal intensities of 60bp probes, tiling the entire genome with 10bp overlaps. RESULTS: It was noted that transcripts corresponding to all the 61 genes were identified in the transcriptome data with varying expression levels ranging from 0.18 to 2.47 fold (normalized with 16SrRNA). The mRNA expression levels of a representative set of seven genes ( four annotated and three hypothetical protein coding genes) were analyzed using quantitative Polymerase Chain Reaction (qPCR) assays with RNA extracted from skin biopsies of 10 newly diagnosed, untreated leprosy cases. It was noted that RNA expression levels were higher for genes involved in homologous recombination whereas the genes with a low level of expression are involved in the direct repair pathway. CONCLUSION: This study provided preliminary information on the potential DNA repair pathways that are extant in M. leprae and the associated genes