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 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

Caffeic Acid Phenethyl Ester Effects: In Silico Study of its Osteoimmunological Mechanisms

2020 ◽  
Vol 17 (5) ◽  
pp. 556-562
Author(s):  
Yuhao Zhao ◽  
Xiaokun Pang ◽  
Akriti Nepal ◽  
Xincan Jiang ◽  
Xiaoxin Xu ◽  
...  
Keyword(s):  
Caffeic Acid ◽  
Biological System ◽  
In Silico ◽  
Target Identification ◽  
Interaction Network ◽  
Caffeic Acid Phenethyl Ester ◽  
New Drugs ◽  
Network Pharmacology ◽  
Drug Target Identification ◽  
Biological Target

Background: Biological system complexity impedes the drug target identification by biological experiments. Thus drugs, rather than acting on target site only, can interact with the entire biological system. Study of this phenomenon, known as network pharmacology, provides grounds for biological target identification of new drugs or acts as a foundation for the discovery of new targets of present drugs. No publication is available on the interaction network of CAPE. Aim: This study was aimed at the investigation of the candidate targets and possible interactions of caffeic acid phenethyl ester (CAPE) involved in its osteoimmunological effects. Methods: This study encompasses the investigation of candidate targets and possible interactions of CAPE by analyzing through PASS Prediction and constructing a biological network of CAPE. Results: In response to input (CAPE), PASS Prediction generated a network of 1723 targets. While selecting the probability to be active (Pa) value greater than 0.7 brought forth only 27 targets for CAPE. Most of these targets predicted the therapeutic role of CAPE as an osteoimmunological agent. Apart from this, this network pharmacology also identified 10 potential anti-cancer targets for CAPE, out of which 7 targets have been used efficiently in developing potent osteoimmunological drugs. Conclusion: This study provides scientific prediction of the mechanisms involved in osteoimmunological effects of CAPE, presenting its promising use in the development of a natural therapeutic agent for the pharmaceutical industry. CAPE targets identified by web-based online databases and network pharmacology need additional in silico assessment such as docking and MD simulation studies and experimental verification to authenticate these results.

scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

scholarly journals Genome-Based Drug Target Identification in Human Pathogen Streptococcus gallolyticus

2021 ◽  
Vol 12 ◽  
Author(s):  
Nosheen Afzal Qureshi ◽  
Syeda Marriam Bakhtiar ◽  
Muhammad Faheem ◽  
Mohibullah Shah ◽  
Ahmed Bari ◽  
...  
Keyword(s):  
Infective Endocarditis ◽  
Drug Targets ◽  
Target Identification ◽  
Acquired Resistance ◽  
Therapeutic Targets ◽  
Streptococcus Gallolyticus ◽  
Cytoplasmic Proteins ◽  
Core Proteins ◽  
Zinc Database ◽  
Drug Target Identification

Streptococcus gallolysticus (Sg) is an opportunistic Gram-positive, non-motile bacterium, which causes infective endocarditis, an inflammation of the inner lining of the heart. As Sg has acquired resistance with the available antibiotics, therefore, there is a dire need to find new therapeutic targets and potent drugs to prevent and treat this disease. In the current study, an in silico approach is utilized to link genomic data of Sg species with its proteome to identify putative therapeutic targets. A total of 1,138 core proteins have been identified using pan genomic approach. Further, using subtractive proteomic analysis, a set of 18 proteins, essential for bacteria and non-homologous to host (human), is identified. Out of these 18 proteins, 12 cytoplasmic proteins were selected as potential drug targets. These selected proteins were subjected to molecular docking against drug-like compounds retrieved from ZINC database. Furthermore, the top docked compounds with lower binding energy were identified. In this work, we have identified novel drug and vaccine targets against Sg, of which some have already been reported and validated in other species. Owing to the experimental validation, we believe our methodology and result are significant contribution for drug/vaccine target identification against Sg-caused infective endocarditis.

scholarly journals Using gene networks to drug target identification

2005 ◽  
Vol 2 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Zhenran Jiang ◽  
Yanhong Zhou
Keyword(s):  
Gene Networks ◽  
Drug Targets ◽  
Gene Network ◽  
Target Identification ◽  
Biological Data ◽  
Potential Drug ◽  
Drug Target Identification ◽  
Novel Drug ◽  
Potential Drug Targets ◽  
Novel Drug Targets

Abstract The complete genome sequences have provided a plethora of potential drug targets. Gene network technique holds the promise of providing a conceptual framework for analysis of the profusion of biological data being generated on potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss some of the network-based approaches for identifying drug targets, with the emphasis on the gene network strategy. In addition, some of the relevant data resources and computational tools are given.

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 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.

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