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 Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Comprehensive Essential Gene Identification as a Platform for Novel Antiinfective Drug Discovery

2002 ◽  
Vol 8 (13) ◽  
pp. 1155-1172 ◽  
Author(s):  
R. Haselbeck ◽  
D. Wall ◽  
B. Jiang ◽  
T. Ketela ◽  
J. Zyskind ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Essential Gene ◽  
Gene Identification

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.

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 Transforming TRP Channel Drug Discovery Using Medium-Throughput Electrophysiological Assays

2013 ◽  
Vol 19 (3) ◽  
pp. 468-477 ◽  
Author(s):  
Jean-Marie Chambard ◽  
Eric Tagat ◽  
Philippe Boudeau ◽  
Michel Partiseti
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Targets ◽  
Transient Receptor Potential ◽  
Inflammatory Diseases ◽  
Receptor Potential ◽  
Target Class ◽  
Screening Assays ◽  
Transient Receptor ◽  
Almost All

Since the cloning of its first member in 1998, transient receptor potential (TRP) cation channels have become one of the most studied ion channel families in drug discovery. These channels, almost all calcium permeant, have been studied in many different (patho)-physiological and therapeutic areas as diverse as pain; neurodegenerative, cardiovascular, and inflammatory diseases; and cancer. At the same time, implementation of automated electrophysiology screening platforms has significantly increased the tractability of ion channels, mainly voltage gated, as drug targets. The work presented in this article shows the design and validation of TRP screening assays using the IonWorks Quattro platform (Molecular Devices, Sunnyvale, CA), allowing a significant increase in throughput to support drug discovery programs. This new player has a direct impact on resources and timelines by prioritizing potential candidates and reducing the number of molecules requiring final testing by manual patch-clamp, which is still today the gold standard technology for this challenging drug target class.

scholarly journals Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery

2003 ◽  
Vol 50 (1) ◽  
pp. 167-181 ◽  
Author(s):  
Terry Roemer ◽  
Bo Jiang ◽  
John Davison ◽  
Troy Ketela ◽  
Karynn Veillette ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Drug Discovery ◽  
Large Scale ◽  
Essential Gene ◽  
Antifungal Drug ◽  
Gene Identification

scholarly journals Advances in omics-based methods to identify novel targets for malaria and other parasitic protozoan infections

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Annie N. Cowell ◽  
Elizabeth A. Winzeler
Keyword(s):  
Drug Discovery ◽  
Antimalarial Drug ◽  
Drug Target ◽  
Drug Targets ◽  
Trna Synthetase ◽  
Parasite Development ◽  
Major Advance ◽  
Liver Stage ◽  
Target Deconvolution ◽  
Novel Targets

Abstract A major advance in antimalarial drug discovery has been the shift towards cell-based phenotypic screening, with notable progress in the screening of compounds against the asexual blood stage, liver stage, and gametocytes. A primary method for drug target deconvolution in Plasmodium falciparum is in vitro evolution of compound-resistant parasites followed by whole-genome scans. Several of the most promising antimalarial drug targets, such as translation elongation factor 2 (eEF2) and phenylalanine tRNA synthetase (PheRS), have been identified or confirmed using this method. One drawback of this method is that if a mutated gene is uncharacterized, a substantial effort may be required to determine whether it is a drug target, a drug resistance gene, or if the mutation is merely a background mutation. Thus, the availability of high-throughput, functional genomic datasets can greatly assist with target deconvolution. Studies mapping genome-wide essentiality in P. falciparum or performing transcriptional profiling of the host and parasite during liver-stage infection with P. berghei have identified potentially druggable pathways. Advances in mapping the epigenomic regulation of the malaria parasite genome have also enabled the identification of key processes involved in parasite development. In addition, the examination of the host genome during infection has identified novel gene candidates associated with susceptibility to severe malaria. Here, we review recent studies that have used omics-based methods to identify novel targets for interventions against protozoan parasites, focusing on malaria, and we highlight the advantages and limitations of the approaches used. These approaches have also been extended to other protozoan pathogens, including Toxoplasma, Trypanosoma, and Leishmania spp., and these studies highlight how drug discovery efforts against these pathogens benefit from the utilization of diverse omics-based methods to identify promising drug targets.

scholarly journals Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Judith Gracia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Biology ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Qsar Model ◽  
Learning Tools ◽  
Active Compounds ◽  
Qsar Modelling ◽  
Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

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.

Understanding Membrane Protein Drug Targets in Computational Perspective

2019 ◽  
Vol 20 (5) ◽  
pp. 551-564 ◽  
Author(s):  
Jianting Gong ◽  
Yongbing Chen ◽  
Feng Pu ◽  
Pingping Sun ◽  
Fei He ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Membrane Protein ◽  
Drug Target ◽  
Drug Targets ◽  
Computational Models ◽  
Drug Repurposing ◽  
Protein Targets ◽  
Target Interaction ◽  
Alternative Understanding

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

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