scholarly journals A Systems-Level Framework for Drug Discovery Identifies Csf1R As A Novel Anti-Epileptic Drug Target

2017 ◽  
Author(s):  
Prashant K Srivastava ◽  
Jonathan van Eyll ◽  
Patrice Godard ◽  
Manuela Mazzuferi ◽  
Benedicte Danis ◽  
...  
Keyword(s):  
Causal Reasoning ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Analytical Framework ◽  
Membrane Receptors ◽  
New Drugs ◽  
Tyrosine Kinase Receptor ◽  
Drug Target Identification ◽  
Novel Therapeutic

ABSTRACTThe identification of mechanistically novel drug targets is highly challenging, particularly for diseases of the central nervous system. To address this problem we developed and experimentally validated a new computational approach to drug target identification that combines gene-regulatory information with a causal reasoning framework (“causal reasoning analytical framework for target discovery” – CRAFT). Starting from gene expression data, CRAFT provides a predictive functional genomics framework for identifying membrane receptors with a direction-specified influence over network expression. As proof-of-concept we applied CRAFT to epilepsy, and predicted the tyrosine kinase receptor Csf1R as a novel therapeutic target for epilepsy. The predicted therapeutic effect of Csf1R blockade was validated in two pre-clinical models of epilepsy using a small molecule inhibitor of Csf1R. These results suggest Csf1R blockade as a novel therapeutic strategy in epilepsy, and highlight CRAFT as a systems-level framework for predicting mechanistically new drugs and targets. CRAFT is applicable to disease settings other than epilepsy.

scholarly journals Analysis of metabolic pathways in mycobacteria to aid drug-target identification

2019 ◽  
Author(s):  
Bridget P. Bannerman ◽  
Sundeep C. Vedithi ◽  
Jorge Júlvez ◽  
Pedro Torres ◽  
Vaishali P. Waman ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Causative Agent ◽  
Metabolic Pathways ◽  
Drug Target ◽  
Drug Targets ◽  
Opportunistic Infections ◽  
Target Identification ◽  
Treatment Strategies ◽  
New Drugs ◽  
Drug Target Identification

AbstractThree related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance.Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO;M.abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; andM.leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential toMycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared byM. tuberculosis, M.lepraeandM.abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared betweenM. tuberculosisandM.abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification.AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

A Review of Recent Advances and Research on Drug Target Identification Methods

2019 ◽  
Vol 20 (3) ◽  
pp. 209-216 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Ningyi Zhang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Topological Features ◽  
Drug Target Identification ◽  
Incomplete Datasets ◽  
Optimal Set

Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods

scholarly journals COMPUTATIONAL IDENTIFICATION OF PUTATIVE DRUG TARGETS IN MALASSEZIA GLOBOSA BY SUBTRACTIVE GENOMICS AND PROTEIN CLUSTER NETWORK APPROACH

2017 ◽  
Vol 9 (9) ◽  
pp. 215
Author(s):  
Ramakrishnan Subhashini ◽  
Muthusamy Jeyam
Keyword(s):  
Drug Targets ◽  
Target Identification ◽  
Interaction Network ◽  
New Drugs ◽  
Treatment Comparison ◽  
Superficial Mycoses ◽  
Cluster Network ◽  
Subtractive Genomics ◽  
Malassezia Globosa ◽  
Drug Target Identification

Objective: Yeast commonly causes superficial mycoses similar to the dermatophytes. Superficial mycoses were reported with an estimated incidence of ∼140,000,000 cases/year worldwide and most frequently caused by Malassezia globosa and Malassezia furfur. Treatment available for these conditions is limited and with side effects. Moreover, termination of the treatment may result in the reoccurrence of the disease. The objective of this research was to identify the putative drug targets using computational approaches.Methods: The analysis of genome sequence improves the understanding of diseases which leads to better treatment. Comparison of the genome of the pathogen with the host at the molecular level is suitable for performing the sequence based prediction of protein-protein interaction network, which also forms the basis of drug target identification leading to the discovery of new drugs for the improved treatment.Results: Out of 100 pathways of M. globosa, 95 were common to the host and 5 were unique to the pathogen. Total common and unique targets from common pathways are 1704 and 300, respectively. A unique target from unique pathways and 147 from common pathways were non-homologous targets. From this, 46 targets were screened out as essential and processed in the next phase to identify the clustered targets which resulted with three clusters based on their biological role and subcellular location.Conclusion: In this study, putative drug targets were identified in M. globosa using in silico approaches of subtractive genomics and cluster network which will help in the next level of drug discovery such as lead identification for the novel targets.

scholarly journals LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

2019 ◽  
Author(s):  
Ilaria Piazza ◽  
Nigel Beaton ◽  
Roland Bruderer ◽  
Thomas Knobloch ◽  
Crystel Barbisan ◽  
...  
Keyword(s):  
Small Molecule ◽  
Binding Sites ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Limited Proteolysis ◽  
Drug Binding ◽  
Homologous Proteins ◽  
Protein Targets ◽  
Drug Target Identification

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in developing optimized small-molecule compounds. Current unbiased approaches cannot directly pinpoint the interaction surfaces between ligands and protein targets. To address his limitation we have developed a new drug target deconvolution approach based on limited proteolysis coupled with mass spectrometry that works across species including human cells (LiP-Quant). LiP-Quant features an automated data analysis pipeline and peptide-level resolution for the identification of any small-molecule binding sites, Here we demonstrate drug target identification by LiP-Quant across compound classes, including compounds targeting kinases and phosphatases. We demonstrate that LiP-Quant estimates the half maximal effective concentration (EC50) of compound binding sites in whole cell lysates. LiP-Quant identifies targets of both selective and promiscuous drugs and correctly discriminates drug binding to homologous proteins. We finally show that the LiP-Quant technology identifies targets of a novel research compound of biotechnological interest.

scholarly journals Comparative analysis of Rosetta stone events in Klebsiella pneumoniae and Streptococcus pneumoniae for drug target identification

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Poornima Ramesh ◽  
Jayashree Honnebailu Nagendrappa ◽  
Santosh Kumar Hulikal Shivashankara
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Functional Role ◽  
Target Identification ◽  
Protein Domain ◽  
Model Parameters ◽  
Signaling Mechanism ◽  
Rosetta Stone ◽  
A Genome ◽  
Drug Target Identification

Abstract Background Drug target identification is a fast-growing field of research in many human diseases. Many strategies have been devised in the post-genomic era to identify new drug targets for infectious diseases. Analysis of protein sequences from different organisms often reveals cases of exon/ORF shuffling in a genome. This results in the fusion of proteins/domains, either in the same genome or that of some other organism, and is termed Rosetta stone sequences. They help link disparate proteins together describing local and global relationships among proteomes. The functional role of proteins is determined mainly by domain-domain interactions and leading to the corresponding signaling mechanism. Putative proteins can be identified as drug targets by re-annotating their functional role through domain-based strategies. Results This study has utilized a bioinformatics approach to identify the putative proteins that are ideal drug targets for pneumonia infection by re-annotating the proteins through position-specific iterations. The putative proteome of two pneumonia-causing pathogens was analyzed to identify protein domain abundance and versatility among them. Common domains found in both pathogens were identified, and putative proteins containing these domains were re-annotated. Among many druggable protein targets, the re-annotation of EJJ83173 (which contains the GFO_IDH_MocA domain) showed that its probable function is glucose-fructose oxidoreduction. This protein was found to have sufficient interactor proteins and homolog in both pathogens but no homolog in the host (human), indicating it as an ideal drug target. 3D modeling of the protein showed promising model parameters. The model was utilized for virtual screening which revealed several ligands with inhibitory activity. These ligands included molecules documented in traditional Chinese medicine and currently marketed drugs. Conclusions This novel strategy of drug target identification through domain-based putative protein re-annotation presents a prospect to validate the proposed drug target to confer its utility as a typical protein targeting both pneumonia-causing species studied herewith.

scholarly journals Elucidating drug targets and mechanisms of action by genetic screens in mammalian cells

2017 ◽  
Vol 53 (53) ◽  
pp. 7162-7167 ◽  
Author(s):  
Martin Kampmann
Keyword(s):  
Mammalian Cells ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Response ◽  
Target Identification ◽  
Mechanisms Of Action ◽  
Genetic Screens ◽  
Crispr Interference ◽  
Genome Wide ◽  
Drug Target Identification

Genome-wide CRISPR interference (CRISPRi) screens in mammalian cells enable drug target identification and uncover genes controlling drug response.

A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets

2017 ◽  
Vol 13 (5) ◽  
pp. 955-969 ◽  
Author(s):  
Mahesh Sharma ◽  
Naeem Shaikh ◽  
Shailendra Yadav ◽  
Sushma Singh ◽  
Prabha Garg
Keyword(s):  
Metabolic Network ◽  
Drug Target ◽  
Drug Targets ◽  
Leishmania Donovani ◽  
Target Identification ◽  
Metabolic Model ◽  
Drug Target Identification ◽  
Antileishmanial Drug ◽  
Network Identification

Development of constraint-based metabolic model forLeishmania donovaniBPK282A1 for drug target identification.

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

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