Mabellini: a genome-wide database for understanding the structural proteome and evaluating prospective antimicrobial targets of the emerging pathogen Mycobacterium abscessus

Similarity of drug targets to human microbiome metaproteome promotes pharmacological promiscuity

Similarity between candidate drug targets and human proteins is commonly assessed to minimize the occurrence of side effects. Although numerous drugs have been found to disrupt the health of the human microbiome, no comprehensive comparison between established drug targets and the human microbiome metaproteome has yet been conducted. Therefore, herein, sequence and structure alignments between human and pathogen drug targets and representative human gut, oral, and vaginal microbiome metaproteomes were performed. Both human and pathogen drug targets were found to be similar in sequence, function, structure, and drug binding capacity to proteins in diverse pathogenic and non-pathogenic bacteria from all three microbiomes. The gut metaproteome was identified as particularly susceptible overall to off-target effects. Certain symptoms, such as infections and immune disorders, may be more common among drugs that non-selectively target host microbiota. These findings suggest that similarities between human microbiome metaproteomes and drug target candidates should be routinely checked.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Mycobacterium abscessus pathogenesis identified by phenogenomic analyses

The medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome. We combined genome-wide association studies with proteome-wide computational structural modelling to define likely causal variants, and employed direct coupling analysis to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings and discover clinically relevant M. abscessus virulence factors including a secretion system, thus illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.

Mycobacterium abscessus: insights from a bioinformatic perspective

Mycobacterium abscessus is a nontuberculous mycobacterium, associated with broncho-pulmonary infections in individuals suffering from cystic fibrosis, bronchiectasis, and pulmonary diseases. The risk factors for transmission include biofilms, contaminated water resources, fomites, and infected individuals. M. abscessus is extensively resistant to antibiotics. To date, there is no vaccine and combination antibiotic therapy is followed. However, drug toxicities, low cure rates, and high cost of treatment make it imperfect. Over the last 20 years, bioinformatic studies on M. abscessus have advanced our understanding of the pathogen. This review integrates knowledge from the analysis of genomes, microbiomes, genomic variations, phylogeny, proteome, transcriptome, secretome, antibiotic resistance, and vaccine design to further our understanding. The utility of genome-based studies in comprehending disease progression, surveillance, tracing transmission routes, and epidemiological outbreaks on a global scale has been highlighted. Furthermore, this review underlined the importance of using computational methodologies for pinpointing factors responsible for pathogen survival and resistance. We reiterate the significance of interdisciplinary research to fight M. abscessus. In a nutshell, the outcome of computational studies can go a long way in creating novel therapeutic avenues to control M. abscessus mediated pulmonary infections.

Management of Mycobacterium avium complex and Mycobacterium abscessus pulmonary disease: therapeutic advances and emerging treatments

Nontuberculous mycobacterial pulmonary disease (NTM-PD) remains a challenging condition to diagnose and treat effectively. Treatment of NTM-PD is prolonged, frequently associated with adverse effects and has variable success. In this review, we consider the factors influencing clinicians when treating NTM-PD and discuss outcomes from key studies on the pharmacological management of Mycobacterium avium complex pulmonary disease and M. abscessus pulmonary disease. We highlight issues relating to treatment-related toxicity and provide an overview of repurposed and emerging therapies for NTM-PD.

Utilizing graph machine learning within drug discovery and development

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.

ProtCHOIR: a tool for proteome-scale generation of homo-oligomers

The rapid developments in gene sequencing technologies achieved in the recent decades, along with the expansion of knowledge on the three-dimensional structures of proteins, have enabled the construction of proteome-scale databases of protein models such as the Genome3D and ModBase. Nevertheless, although gene products are usually expressed as individual polypeptide chains, most biological processes are associated with either transient or stable oligomerisation. In the PDB databank, for example, ~40% of the deposited structures contain at least one homo-oligomeric interface. Unfortunately, databases of protein models are generally devoid of multimeric structures. To tackle this particular issue, we have developed ProtCHOIR, a tool that is able to generate homo-oligomeric structures in an automated fashion, providing detailed information for the input protein and output complex. ProtCHOIR requires input of either a sequence or a protomeric structure that is queried against a pre-constructed local database of homo-oligomeric structures, then extensively analyzed using well-established tools such as PSI-Blast, MAFFT, PISA and Molprobity. Finally, MODELLER is employed to achieve the construction of the homo-oligomers. The output complex is thoroughly analyzed taking into account its stereochemical quality, interfacial stabilities, hydrophobicity and conservation profile. All these data are then summarized in a user-friendly HTML report that can be saved or printed as a PDF file. The software is easily parallelizable and also outputs a comma-separated file with summary statistics that can straightforwardly be concatenated as a spreadsheet-like document for large-scale data analyses. As a proof-of-concept, we built oligomeric models for the Mabellini Mycobacterium abscessus structural proteome database. ProtCHOIR can be run as a web-service and the code can be obtained free-of-charge at http://lmdm.biof.ufrj.br/protchoir.

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.

In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology

Tuberculosis (TB) kills more individuals in the world than any other disease, and a threat made direr by the coverage of drug-resistant strains of Mycobacterium tuberculosis (Mtb). Bacillus Calmette-Guérin (BCG) is the single TB vaccine licensed for use in human beings and effectively protects infants and children against severe military and meningeal TB. We applied advanced computational techniques to develop a universal TB vaccine. In the current study, we select the very conserved, experimentally confirmed Mtb antigens, including Rv2608, Rv2684, Rv3804c (Ag85A), and Rv0125 (Mtb32A) to design a novel multi-epitope subunit vaccine. By using the Immune Epitopes Database (IEDB), we predicted different B-cell and T-cell epitopes. An adjuvant (Griselimycin) was also added to vaccine construct to improve its immunogenicity. Bioinformatics tools were used to predict, refined, and validate the 3D structure and then docked with toll-like-receptor (TLR-3) using different servers. The constructed vaccine was used for further processing based on allergenicity, antigenicity, solubility, different physiochemical properties, and molecular docking scores. The in silico immune simulation results showed significant response for immune cells. For successful expression of the vaccine in E. coli, in-silico cloning and codon optimization were performed. This research also sets out a good signal for the design of a peptide-based tuberculosis vaccine. In conclusion, our findings show that the known multi-epitope vaccine may activate humoral and cellular immune responses and maybe a possible tuberculosis vaccine candidate. Therefore, more experimental validations should be exposed to it.

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 approaches in epitope design using DNA binding proteins as vaccine candidate in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is a successful pathogen in the history of mankind. A high rate of mortality and morbidity raises the need for vaccine development. Mechanism of pathogenesis, survival strategy and virulence determinant are needed to be explored well for this pathogen. The involvement of DNA binding proteins in the regulation of virulence genes, transcription, DNA replication, repair make them more significant. In present work, we have identified 1453 DNA binding proteins (DBPs) in the 4173 genes of Mtb through the DNABIND tool and they were subjected for further screening by incorporating different bioinformatics tools. The eighteen DBPs were selected for the B-cell epitope prediction by using ABCpred server. Moreover, the B-cell epitope bearing the antigenic and non- allergenic property were selected for T-cell epitope prediction using ProPredI, and ProPred server. Finally, DGIGSAVSV (Rv1088), IRALPSSRH (Rv3923c), LTISPIANS (Rv3235), VQPSGKGGL (Rv2871) VPRPGPRPG (Rv2731) and VGQKINPHG (Rv0707) were identified as T-cell epitopes. The structural modelling of these epitopes and DBPs was performed to ensure the localization of these epitopes on the respective proteins. The interaction studies of these epitopes with human HLA confirmed their validation to be used as potential vaccine candidates. Collectively, these results revealed that the DBPs- Rv2731, Rv3235, Rv1088, Rv0707, Rv3923c and Rv2871 are the most appropriate vaccine candidates. In our knowledge, it is the first report of using the DBPs of Mtb for epitope prediction. Significantly, this study also provides evidence to be useful for designing a peptide-based vaccine against tuberculosis.

A Personal History of Using Crystals and Crystallography to Understand Biology and Advanced Drug Discovery

Over the past 60 years, the use of crystals to define structures of complexes using X-ray analysis has contributed to the discovery of new medicines in a very significant way. This has been in understanding not only small-molecule inhibitors of proteins, such as enzymes, but also protein or peptide hormones or growth factors that bind to cell surface receptors. Experimental structures from crystallography have also been exploited in software to allow prediction of structures of important targets based on knowledge of homologues. Crystals and crystallography continue to contribute to drug design and provide a successful example of academia–industry collaboration.

Emerging opportunities of exploiting mycobacterial electron transport chain pathway for drug-resistant tuberculosis drug discovery

Introduction: Tuberculosis (TB) is a leading infectious disease worldwide whose chemotherapy is challenged by the continued rise of drug resistance. This epidemic urges the need to discover anti-TB drugs with novel modes of action.Areas covered: The mycobacterial electron transport chain (ETC) pathway represents a hub of anti-TB drug targets. Herein, the authors highlight the various targets within the mycobacterial ETC and highlight some of the promising ETC-targeted drugs and clinical candidates that have been discovered or repurposed. Furthermore, recent breakthroughs in the availability of X-ray and/or cryo-EM structures of some targets are discussed, and various opportunities of exploiting these structures for the discovery of new anti-TB drugs are emphasized.Expert opinion: The drug discovery efforts targeting the ETC pathway have led to the FDA approval of bedaquiline, a F_OF₁-ATP synthase inhibitor, and the discovery of Q203, a clinical candidate drug targeting the mycobacterial cytochrome bcc-aa3 supercomplex. Moreover, clofazimine, a proposed prodrug competing with menaquinone for its reduction by mycobacterial NADH dehydrogenase 2, has been repurposed for TB treatment. Recently available structures of the mycobacterial ATP synthase C9 rotary ring and the cytochrome bcc-aa3 supercomplex represent further opportunities for the structure-based drug design (SBDD) of the next-generation of inhibitors against Mycobacterium tuberculosis.