In Silico Drug Target Discovery Through Proteome Mining from M. tuberculosis: An Insight into Antivirulent Therapy

2020 ◽  
Vol 23 (3) ◽  
pp. 253-268
Author(s):  
Shreya Bhattacharya ◽  
Puja Ghosh ◽  
Debasmita Banerjee ◽  
Arundhati Banerjee ◽  
Sujay Ray
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virulence Factors ◽  
Spectrum Analysis ◽  
Drug Target ◽  
Drug Targets ◽  
Physicochemical Characterization ◽  
Specific Drug ◽  
Hypothetical Proteins ◽  
Drug Target Discovery ◽  
Domain Annotation

Aim and Objective: One of the challenges to conventional therapies against Mycobacterium tuberculosis is the development of multi-drug resistant pathogenic strains. This study was undertaken to explore new therapeutic targets for the revolutionary antivirulence therapy utilizing the pathogen’s essential hypothetical proteins, serving as virulence factors, which is the essential first step in novel drug designing. Methods: Functional annotations of essential hypothetical proteins from Mycobacterium tuberculosis (H37Rv strain) were performed through domain annotation, Gene Ontology analysis, physicochemical characterization and prediction of subcellular localization. Virulence factors among the essential hypothetical proteins were predicted, among which pathogen-specific drug target candidates, non-homologous to human and gut microbiota, were identified. This was followed by druggability and spectrum analysis of the identified targets. Results and conclusion: The study successfully assigned functions of 83 essential hypothetical proteins of Mycobacterium tuberculosis, among which 25 were identified as virulence factors. Out of 25, 12 virulence factors were observed as potential pathogen-specific drug target candidates. Nine potential targets had druggable properties and rest three were considered as novel targets. Exploration of these targets will provide new insights into future drug development. Characterization of subcellular localizations revealed that most of the predicted targets were cytoplasmic which could be ideal for intracellular drugs, while two drug targets were membranebound, ideal for vaccines. Spectrum analysis identified one broad-spectrum and 11 narrowspectrum targets. This study would, therefore, instigate designing novel therapeutics for antivirulence therapy, which have the potential to serve as revolutionary treatment instead of conventional antibiotic therapies to overcome the lethality of antibiotic-resistant strains.

scholarly journals An omics perspective on drug target discovery platforms

2019 ◽  
Vol 21 (6) ◽  
pp. 1937-1953 ◽  
Author(s):  
Jussi Paananen ◽  
Vittorio Fortino
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Knowledge Bases ◽  
Scientific Data ◽  
Data Sources ◽  
Biomedical Data ◽  
Drug Target Discovery ◽  
Data Repositories ◽  
Target Discovery ◽  
Omics Technologies

Abstract The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

scholarly journals Unbiased Identification of Extracellular Protein–Protein Interactions for Drug Target and Biologic Drug Discovery

2021 ◽  
Author(s):  
Shengya Cao ◽  
Nadia Martinez-Martin
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Extracellular Protein ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Drug Target Discovery ◽  
Technological Improvements ◽  
Targeted Drug Discovery

Technological improvements in unbiased screening have accelerated drug target discovery. In particular, membrane-embedded and secreted proteins have gained attention because of their ability to orchestrate intercellular communication. Dysregulation of their extracellular protein–protein interactions (ePPIs) underlies the initiation and progression of many human diseases. Practically, ePPIs are also accessible for modulation by therapeutics since they operate outside of the plasma membrane. Therefore, it is unsurprising that while these proteins make up about 30% of human genes, they encompass the majority of drug targets approved by the FDA. Even so, most secreted and membrane proteins remain uncharacterized in terms of binding partners and cellular functions. To address this, a number of approaches have been developed to overcome challenges associated with membrane protein biology and ePPI discovery. This chapter will cover recent advances that use high-throughput methods to move towards the generation of a comprehensive network of ePPIs in humans for future targeted drug discovery.

scholarly journals Crystal Structure of Mycobacterium tuberculosis Elongation Factor G1

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaopan Gao ◽  
Xia Yu ◽  
Kaixiang Zhu ◽  
Bo Qin ◽  
Wei Wang ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Drug Targets ◽  
Elongation Factor ◽  
Protein Translation ◽  
Crystal Form ◽  
Multidrug Resistant ◽  
Major Focus ◽  
Trna Translocation

Mycobacterium tuberculosis (Mtb) caused an estimated 10 million cases of tuberculosis and 1.2 million deaths in 2019 globally. The increasing emergence of multidrug-resistant and extensively drug-resistant Mtb is becoming a public health threat worldwide and makes the identification of anti-Mtb drug targets urgent. Elongation factor G (EF-G) is involved in tRNA translocation on ribosomes during protein translation. Therefore, EF-G is a major focus of structural analysis and a valuable drug target of antibiotics. However, the crystal structure of Mtb EF-G1 is not yet available, and this has limited the design of inhibitors. Here, we report the crystal structure of Mtb EF-G1 in complex with GDP. The unique crystal form of the Mtb EF-G1-GDP complex provides an excellent platform for fragment-based screening using a crystallographic approach. Our findings provide a structure-based explanation for GDP recognition, and facilitate the identification of EF-G1 inhibitors with potential interest in the context of drug discovery.

Applications of Machine Learning in Drug Target Discovery

2020 ◽  
Vol 21 (10) ◽  
pp. 790-803 ◽  
Author(s):  
Dongrui Gao ◽  
Qingyuan Chen ◽  
Yuanqi Zeng ◽  
Meng Jiang ◽  
Yongqing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Modern Drug ◽  
Applications Of Machine Learning

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

Essential gene identification and drug target prioritization in Leishmania species

2014 ◽  
Vol 10 (5) ◽  
pp. 1184-1195 ◽  
Author(s):  
M. L. Stanly Paul ◽  
Amandeep Kaur ◽  
Ankit Geete ◽  
M. Elizabeth Sobhia
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Targets ◽  
Essential Gene ◽  
Leishmania Species ◽  
Gene Identification ◽  
Specific Drug

New stage specific drug targets for contemporary drug discovery for leishmaniasis.

scholarly journals Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasiteLeishmania

2016 ◽  
Vol 113 (21) ◽  
pp. E3012-E3021 ◽  
Author(s):  
Élodie Gazanion ◽  
Christopher Fernández-Prada ◽  
Barbara Papadopoulou ◽  
Philippe Leprohon ◽  
Marc Ouellette
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Screening Method ◽  
Resistance Mechanisms ◽  
Phospholipid Metabolism ◽  
Hypothetical Proteins ◽  
Drug Selection ◽  
Innovative Strategies ◽  
Generation Sequencing ◽  
Sensitive Method

Innovative strategies are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. Here we develop a sensitive method, which we term Cosmid Sequencing (or “Cos-Seq”), based on functional cloning coupled to next-generation sequencing. Cos-Seq identified >60 loci in theLeishmaniagenome that were enriched via drug selection with methotrexate and five major antileishmanials (antimony, miltefosine, paromomycin, amphotericin B, and pentamidine). Functional validation highlighted both known and previously unidentified drug targets and resistance genes, including novel roles for phosphatases in resistance to methotrexate and antimony, for ergosterol and phospholipid metabolism genes in resistance to miltefosine, and for hypothetical proteins in resistance to paromomycin, amphothericin B, and pentamidine. Several genes/loci were also found to confer resistance to two or more antileishmanials. This screening method will expedite the discovery of drug targets and resistance mechanisms and is easily adaptable to other microorganisms.

scholarly journals Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

2019 ◽  
Vol 35 (16) ◽  
pp. 2818-2826 ◽  
Author(s):  
Jinyan Chan ◽  
Xuan Wang ◽  
Jacob A Turner ◽  
Nicole E Baldwin ◽  
Jinghua Gu
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Effective Means ◽  
Drug Repurposing ◽  
Superior Performance ◽  
Supplementary Information ◽  
Breast Cancer Dataset ◽  
Specific Drug ◽  
Cancer Dataset ◽  
Disease Specific

Abstract Motivation Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. Availability and implementation Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Comparative Proteome Analysis of Mycobacterium Tuberculosis Strains - H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A Step Forward to Identify Novel Drug Targets

2020 ◽  
Vol 17 (11) ◽  
pp. 1422-1431
Author(s):  
Shradheya R.R. Gupta ◽  
Ekta Gupta ◽  
Avnam Ohri ◽  
Sandeep Kumar Shrivastava ◽  
Sumita Kachhwaha ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Drug Targets ◽  
Virulent Strain ◽  
Proteome Analysis ◽  
Target Protein ◽  
Central Asian ◽  
Virulent Strains ◽  
Novel Drug ◽  
Novel Drug Targets

Background: Mycobacterium tuberculosis is a causative agent of tuberculosis. It is a non-motile, acid-fast, obligatory aerobic bacterium. Finding novel drug targets in Mycobacterium tuberculosis has become extremely important as the bacterium is evolving into a more dangerous multi-drug resistant pathogen. The predominant strains in India belong to the Central-Asian, East- African Indian, and Beijing clad. For the same reason, the whole proteomes of a non-virulent strain (H37Ra), a virulent (H37Rv) and two clinical strains, a Central-Asian clad (CAS/NITR204) and a Beijing clad (CCDC5180) have been selected for comparative study. Selecting a phylogenetically close and majorly studied non-virulent strain is helpful in removing the common and undesired proteins from the study. Objective: The study compares the whole proteome of non-virulent strain with the other three virulent strains to find a unique protein responsible for virulence in virulent strains. It is expected that the drugs developed against identified targets will be specific to the virulent strains. Additionally, to assure minimal toxicity to the host, we also screened the human proteome. Methods: Comparative proteome analysis was used for target identification and in silico validation of identified target protein Rv2466c, identification of the respective ligand of the identified target protein and binding interaction study using Molecular docking and Molecular Dynamic Simulation study were used in this study. Results and Discussion: Finally, eleven proteins were found to be unique in virulent strain only and out of which, Rv2466c (PDB-ID: 4ZIL) was found to be an essential protein and identified as a putative drug target protein for further study. The compound glutathione was found to be a suitable inhibitor for Rv2466c. In this study, we used a comparative proteomics approach to identify novel target proteins. Conclusion: This study is unique as we are assured that the study will move forward the research in a new direction to cure the deadly disease (tuberculosis) caused by Mycobacterium tuberculosis. Rv2466c was identified as a novel drug target and glutathione as a respective ligand of Rv2466c. Discovery of the novel drug target as well as the drug will provide a solution to drug resistance as well as the infection caused by Mycobacterium tuberculosis.

scholarly journals Comparative Genome and Network Centrality Analysis to Identify Drug Targets ofMycobacterium tuberculosis H37Rv

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Tilahun Melak ◽  
Sunita Gakkhar
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Drug Targets ◽  
Tuberculosis H37rv ◽  
Network Centrality ◽  
Potential Drug ◽  
Comparative Genome ◽  
Mycobacterium Tuberculosis H37rv ◽  
Centrality Analysis ◽  
Potential Drug Targets

Potential drug targets ofMycobacterium tuberculosis H37Rvwere identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome ofMycobacterium tuberculosis H37Rvagainst Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

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