scholarly journals TargetSeeker-MS: A Computational Method for Drug Target Discovery using Protein Separation Coupled to Mass Spectrometry

2019 ◽  
Author(s):  
Mathieu Lavallée-Adam ◽  
Alexander Pelletier ◽  
Jolene K. Diedrich ◽  
William Low ◽  
Antonio F. M. Pinto ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Separation ◽  
Computational Method ◽  
Drug Target Discovery ◽  
C Elegans ◽  
Computational Analyses ◽  
Novel Drug

ABSTRACTWhen coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug target predictions made by these methods have remained rudimentary and significantly differ depending on the protocol used to produce the data. To identify drug targets in datasets produced using different EBPS-MS techniques, we have developed a novel flexible computational approach named TargetSeeker-MS. We showed that TargetSeeker-MS reproducibly identifies known and novel drug targets in C. elegans and HEK293 samples that were treated with the fungicide benomyl and processed using two different EBPS techniques. We also validated a novel benomyl target in vitro. TargetSeeker-MS, which is available online, allows for the confident identification of targets of a drug on a proteome scale, thereby facilitating the evaluation of its clinical viability.

Innovative Methodology in the Discovery of Novel Drug Targets in the Free-Living Amoebae

2018 ◽  
Vol 20 (1) ◽  
pp. 60-69 ◽  
Author(s):  
Abdul Mannan Baig
Keyword(s):  
Drug Targets ◽  
Mortality Rates ◽  
In Vitro Assays ◽  
Cancer Drug ◽  
Cns Infection ◽  
Free Living ◽  
Drug Target Discovery ◽  
Novel Drug ◽  
Novel Drug Targets

Despite advances in drug discovery and modifications in the chemotherapeutic regimens, human infections caused by free-living amoebae (FLA) have high mortality rates (~95%). The FLA that cause fatal human cerebral infections include Naegleria fowleri, Balamuthia mandrillaris and Acanthamoeba spp. Novel drug-target discovery remains the only viable option to tackle these central nervous system (CNS) infection in order to lower the mortality rates caused by the FLA. Of these FLA, N. fowleri causes primary amoebic meningoencephalitis (PAM), while the A. castellanii and B. Mandrillaris are known to cause granulomatous amoebic encephalitis (GAE). The infections caused by the FLA have been treated with drugs like Rifampin, Fluconazole, Amphotericin-B and Miltefosine. Miltefosine is an anti-leishmanial agent and an experimental anti-cancer drug. With only rare incidences of success, these drugs have remained unsuccessful to lower the mortality rates of the cerebral infection caused by FLA. Recently, with the help of bioinformatic computational tools and the discovered genomic data of the FLA, discovery of newer drug targets has become possible. These cellular targets are proteins that are either unique to the FLA or shared between the humans and these unicellular eukaryotes. The latter group of proteins has shown to be targets of some FDA approved drugs prescribed in non-infectious diseases. This review out-lines the bioinformatics methodologies that can be used in the discovery of such novel drug-targets, their chronicle by in-vitro assays done in the past and the translational value of such target discoveries in human diseases caused by FLA.

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.

scholarly journals 4E Interacting Protein as a Potential Novel Drug Target for Nucleoside Analogues in Trypanosoma Brucei

2021 ◽  
Vol 9 (4) ◽  
pp. 826
Author(s):  
Dorien Mabille ◽  
Camila Cardoso Santos ◽  
Rik Hendrickx ◽  
Mathieu Claes ◽  
Peter Takac ◽  
...  
Keyword(s):  
Mode Of Action ◽  
Drug Target ◽  
Drug Targets ◽  
De Novo ◽  
Treatment Options ◽  
Adenosine Kinase ◽  
Nucleoside Analogues ◽  
Purine Salvage ◽  
Interacting Protein ◽  
Novel Drug

Human African trypanosomiasis is a neglected parasitic disease for which the current treatment options are quite limited. Trypanosomes are not able to synthesize purines de novo and thus solely depend on purine salvage from the host environment. This characteristic makes players of the purine salvage pathway putative drug targets. The activity of known nucleoside analogues such as tubercidin and cordycepin led to the development of a series of C7-substituted nucleoside analogues. Here, we use RNA interference (RNAi) libraries to gain insight into the mode-of-action of these novel nucleoside analogues. Whole-genome RNAi screening revealed the involvement of adenosine kinase and 4E interacting protein into the mode-of-action of certain antitrypanosomal nucleoside analogues. Using RNAi lines and gene-deficient parasites, 4E interacting protein was found to be essential for parasite growth and infectivity in the vertebrate host. The essential nature of this gene product and involvement in the activity of certain nucleoside analogues indicates that it represents a potential novel drug target.

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 A transcriptomics-guided drug target discovery strategy identifies novel receptor ligands for lung regeneration

2021 ◽  
Author(s):  
Xinhui Wu ◽  
Sophie Bos ◽  
Thomas M Conlon ◽  
Meshal Ansari ◽  
Vicky Verschut ◽  
...  
Keyword(s):  
Cigarette Smoke ◽  
Drug Target ◽  
Therapeutic Potential ◽  
Chronic Obstructive ◽  
Sequencing Analysis ◽  
Receptor Ligands ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Alveolar Epithelial

Currently, there is no pharmacological treatment targeting defective tissue repair in chronic disease. Here we utilized a transcriptomics-guided drug target discovery strategy using gene signatures of smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke, identifying druggable targets expressed in alveolar epithelial progenitors of which we screened the function in lung organoids. We found several drug targets with regenerative potential of which EP and IP prostanoid receptor ligands had the most significant therapeutic potential in restoring cigarette smoke-induced defects in alveolar epithelial progenitors in vitro and in vivo. Mechanistically, we discovered by using scRNA-sequencing analysis that circadian clock and cell cycle/apoptosis signaling pathways were enriched in alveolar epithelial progenitor cells in COPD patients and in a relevant model of COPD, which was prevented by PGE2 or PGI2 mimetics. Conclusively, specific targeting of EP and IP receptors offers therapeutic potential for injury to repair in COPD.

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.

Heterogeneous nuclear ribonucleoprotein A2B1, a novel drug target for hepatitis B virus infection

2021 ◽  
Author(s):  
Min Xu ◽  
Yu Chen ◽  
Hao-Yan Yuan ◽  
Yue-Hai Shen ◽  
Jia-Yao Xiang ◽  
...  
Keyword(s):  
Drug Target ◽  
Hbv Infection ◽  
Type I Interferons ◽  
Type I ◽  
Heterogeneous Nuclear Ribonucleoprotein ◽  
Deep Groove ◽  
Nuclear Ribonucleoprotein ◽  
Novel Drug

Abstract HBV infection is a major global health burden that needs novel immunotherapeutic approaches. Herein, we show that heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) is a novel drug target for HBV infection. We reveal the new target with highly selective probes of PAC5, a natural sesquiterpene derivative. PAC5 show potent anti-HBV activity in vivo and in vitro. Further studies on its mode of action indicate that PAC5 binds to the residue Asp49 and a deep groove in the RNA recognition motif1 (RRM1) region of hnRNPA2B1. PAC5-bound hnRNPA2B1 is activated, dimerized, and translocated to the cytoplasm where it activates the TBK1-IRF3 pathway, leading to the production of type I interferons (IFNs). Furthermore, PAC5 also suppresses other viral replications, such as SARS-CoV-2 and vesicular stomatitis virus (VSV). Our results indicate that PAC5 is the first small molecule agonist of hnRNPA2B1, a drug target potentially valid for broad-spectrum viral infections, providing a novel strategy for viral immunotherapy.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

scholarly journals Molecular Network Approach Reveals Rictor as a Central Target of Cardiac ProtectomiRs

2021 ◽  
Vol 22 (17) ◽  
pp. 9539
Author(s):  
András Makkos ◽  
Bence Ágg ◽  
Zoltán V. Varga ◽  
Zoltán Giricz ◽  
Mariann Gyöngyösi ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Ischemic Postconditioning ◽  
Molecular Network ◽  
Pig Model ◽  
Node Degree ◽  
Central Target ◽  
Mrna Targets ◽  
Novel Drug

Cardioprotective medications are still unmet clinical needs. We have previously identified several cardioprotective microRNAs (termed ProtectomiRs), the mRNA targets of which may reveal new drug targets for cardioprotection. Here we aimed to identify key molecular targets of ProtectomiRs and confirm their association with cardioprotection in a translational pig model of acute myocardial infarction (AMI). By using a network theoretical approach, we identified 882 potential target genes of 18 previously identified protectomiRs. The Rictor gene was the most central and it was ranked first in the protectomiR-target mRNA molecular network with the highest node degree of 5. Therefore, Rictor and its targeting microRNAs were further validated in heart samples obtained from a translational pig model of AMI and cardioprotection induced by pre- or postconditioning. Three out of five Rictor-targeting pig homologue of rat ProtectomiRs showed significant upregulation in postconditioned but not in preconditioned pig hearts. Rictor was downregulated at the mRNA and protein level in ischemic postconditioning but not in ischemic preconditioning. This is the first demonstration that Rictor is the central molecular target of ProtectomiRs and that decreased Rictor expression may regulate ischemic postconditioning-, but not preconditioning-induced acute cardioprotection. We conclude that Rictor is a potential novel drug target for acute cardioprotection.

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