Computational de-orphaning of Mycobacterium tuberculosis 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

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

Decoding the similarities and differences among mycobacterial species

Mycobacteriaceae comprises pathogenic species such as Mycobacterium tuberculosis, M. leprae and M. abscessus, as well as non-pathogenic species, for example, M. smegmatis and M. thermoresistibile. Genome comparison and annotation studies provide insights into genome evolutionary relatedness, identify unique and pathogenicity-related genes in each species, and explore new targets that could be used for developing new diagnostics and therapeutics. Here, we present a comparative analysis of ten-mycobacterial genomes with the objective of identifying similarities and differences between pathogenic and non-pathogenic species. We identified 1080 core orthologous clusters that were enriched in proteins involved in amino acid and purine/pyrimidine biosynthetic pathways, DNA-related processes (replication, transcription, recombination and repair), RNA-methylation and modification, and cell- wall polysaccharide biosynthetic pathways. For their pathogenicity and survival in the host cell, pathogenic species have gained specific sets of genes involved in repair and protection of their genomic DNA. M. leprae is of special interest owing to its having the smallest genome (1600 genes and ~1300 psuedogenes), yet poor genome annotation. More than 75% of the pseudogenes were found to have a functional ortholog in the other mycobacterial genomes and belong to protein families such as transferases, oxidoreductases and hydrolases.This work was supported by MRC Newton Award (RG78439: SM, TLB), Programme Grant (093167/Z/10/Z: TLB), Cystic Fibrosis Trust Grant (RG70975) and Wellcome Trust Investigator Award (200814/Z/16/Z: TLB), American Leprosy Mission (RG88726: SCV). Funding for open access charge: [MRC Newton Award/ RG78439]

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

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 characterised 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 imple- mented 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.This work is supported and funded by King Abdullah scholarship (Saudi Arabia research coun- cil), and American Leprosy Missions grants (G88726), SET is funded by the Cystic Fibrosis Trust (RG 70975) and Fondation Botnar (RG91317). A.R.J is funded by the Biotechnology and Biological Sciences Research Council (BBSRC) DTP studentship (BB/M011194/1). B.B. is funded by the Cystic Fibrosis Trust and L.C. on a studentship from Ipsen. T.L.B. is funded by a the Wellcome Trust Investigator Award, PHZJ/489 RG83114 (2016-2021

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

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