scholarly journals Subtractive genomics approach in identifying polysacharide biosynthesis protein as novel drug target against Eubacterium nodatum

2019 ◽  
Vol 5 (2) ◽  
pp. 382-392 ◽  
Author(s):  
Shilpa S. Shiragannavar ◽  
Arun K. Shettar ◽  
Shivakumar B. Madagi ◽  
Sunanda Sarawad
Keyword(s):  
Drug Target ◽  
Subtractive Genomics ◽  
Subtractive Genomics Approach ◽  
Novel Drug

scholarly journals Virtual Screening and Docking Studies of Identified Potential Drug Target: Polysaccharide Deacetylase in Bacillus anthracis

2015 ◽  
Vol 34 ◽  
pp. 70-77
Author(s):  
K. Zaveri ◽  
A. Krishna Chaitanya ◽  
I. Bhaskar Reddy
Keyword(s):  
Virtual Screening ◽  
Bacillus Anthracis ◽  
Drug Target ◽  
Drug Targets ◽  
Docking Studies ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Subtractive Genomics ◽  
Subtractive Genomics Approach ◽  
Novel Drug

In recent years, insilico approaches have been predicting novel drug targets. The present day development in pharmaceutics mainly ponders on target based drugs and this has been aided by structure based drug designing and subtractive genomics. In the present study, the computational genome subtraction methodology was applied for identification of novel, potential drug target against Bacillus anthracis, cause of deadly anthrax. The potential drug target identified through subtractive genomics approach was considered as polysaccharide deacetylase. By virtual screening against NCI database and Drugbank chemical libraries, two potential lead molecules were predicted. Further the potential lead molecules and target protein were subjected for docking studies using Autodock.

Potential Drug Targets Identification against Clostridioides difficile (CD) and Characterization of Indispensable Proteins by a Subtractive Genomics Approach Followed by Virtual Screening

2021 ◽  
Vol 18 ◽  
Author(s):  
Reaz Uddin ◽  
Alina Arif
Keyword(s):  
Drug Targets ◽  
Drug Resistant ◽  
Potential Drug ◽  
Drug Candidates ◽  
Subtractive Genomics ◽  
Clostridioides Difficile ◽  
Subtractive Genomics Approach ◽  
Novel Drug ◽  
Potential Drug Targets ◽  
Novel Drug Targets

Background: Clostridioides difficile (CD) is a multi-drug resistant, enteric pathogenic bacterium. The CD associated infections are the leading cause of nosocomial diarrhea that can further lead to pseudomembranous colitis up to a toxic mega-colon or sepsis with greater mortality and morbidity risks. The CD infection possess higher rates of recurrence due to its greater resistance against antibiotics. Considering its higher rates of recurrence, it has become a major burden on the healthcare facilities. Therefore, there is a dire need to identify novel drug targets to combat with the antibiotic resistance of Clostridioides difficile. Objective: To identify and propose new and novel drug targets against the Clostridioides difficile. Methods: In the current study, a computational subtractive genomics approach was applied to obtain a set of potential drug targets that exists in the multi-drug resistant strain of Clostridioides difficile. Here, the uncharacterized proteins were studied as potential drug targets. The methodology involved several bioinformatics databases and tools. The druggable proteins sequences were retrieved based on non-homology with host proteome and essentiality for the survival of the pathogen. The uncharacterized proteins were functionally characterized using different computational tools and sub-cellular localization was also predicted. The metabolic pathways were analyzed using KEGG database. Eventually, the druggable proteome has been fetched using sequence similarity with the already available drug targets present in DrugBank database. These druggable proteins were further explored for the structural details to identify drug candidates. Results : A priority list of potential drug targets was provided with the help of the applied method on complete proteome set of the C. difficile. Moreover, the drug like compounds have been screened against the potential drug targets to prioritize potential drug candidates. To facilitate the need for drug targets and therapies, the study proposed five potential protein drug targets out of which three proposed drug targets were subjected to homology modeling to explore their structural and functional activities. Conclusion: In conclusion, we proposed three unique, unexplored drug targets against C. difficile. The structure-based methods were applied and resulted in a list of top scoring compounds as potential inhibitors to proposed drug targets.

scholarly journals Identification of potential candidates for drug designing against Klebsiella Pneumoniae among hypothetical proteins of DNA Adenine Methyltransferase through subtractive genomics approach

2020 ◽  
Author(s):  
Umairah Natasya Mohd Omeershffudin ◽  
Suresh Kumar
Keyword(s):  
Klebsiella Pneumoniae ◽  
Drug Target ◽  
Bacterial Pathogens ◽  
Hypothetical Proteins ◽  
Homology Analysis ◽  
Subtractive Genomics ◽  
Natural Inhibitor ◽  
Subtractive Genomics Approach ◽  
Dna Adenine Methyltransferase ◽  
Selection Of

ABSTRACTKlebsiella Pneumoniae is a gram-negative bacterium that is known for causing infection in nosocomial settings. As reported by WHO, this bacterial pathogen is classified as an urgent threat our most concern is that these bacterial pathogens acquired genetic traits that make them resistant towards antibiotics. The last class of antibiotics; carbapenems are not able to combat these bacterial pathogens allowing them to clonally expand their antibiotic-resistant strain. Most antibiotics target the essential pathways of the bacteria cell however these targets are no longer susceptible to the antibiotic. Hence in our study, we focus on Klebsiella Pneumoniae bacterial strains that contain DNA Adenine Methyltransferase domain which suggests a new potential site for a drug target. DNA methylation is seen to regulate the attenuation of bacterial virulence. In this study, all hypothetical proteins of Klebsiella Pneumoniae containing N6 DNA Adenine Methyltransferase domain were analysed for a potential drug target. About 32 hypothetical proteins were retrieved from Uniprot. 19 proteins were selected through a step-wise subtractive genomics approach like a selection of non-homologus proteins against the human host, selection of bacterial proteins contains an essential gene, broad-spectrum analysis, druggability analysis, Non-homology analysis against gut microbiota. Through drug target prioritization like sub-cellular analysis, drug property analysis, anti-target non-homology analysis, virulence factor analysis and protein-protein interaction analysis one drug target protein (Uniprot ID: A0A2U0NNR3) was prioritized. Identified drug target docked with potential inhibitors like are mahanine (PubChem ID: 375151), curcumin (PubChem ID: 969516), EGCG (PubChem ID: 65064), nanaomycin A (PubChem ID: 40586), parthenolide (PubChem ID: 7251185), quercetin (PubChem ID: 5280343) and trimethylaurintricarboxylic acid. Based on the moelcular docking analysis, mahanine has the highest binding affinity. In order to identify novel natural inhibitor based on mahanine fingerprint search is performed against NPASS (Natural Product Activity and Species Source databases) and Koenimbine was identified as a novel natural inhibitor based on virtual screening.

Identification of Antineoplastic Targets with Systems Approaches, Using Resveratrol as an In-Depth Case Study

2017 ◽  
Vol 23 (32) ◽  
pp. 4773-4793 ◽  
Author(s):  
Nivedita Singh ◽  
Sherry Freiesleben ◽  
Olaf Wolkenhauer ◽  
Yogeshwer Shukla ◽  
Shailendra K. Gupta
Keyword(s):  
Drug Target ◽  
Complex Disease ◽  
Biological Targets ◽  
Systems Approaches ◽  
Anticancer Mechanism ◽  
Genetic And Environmental Factors ◽  
Key Steps ◽  
Network Approaches ◽  
Novel Drug

The identification and validation of novel drug–target combinations are key steps in the drug discovery processes. Cancer is a complex disease that involves several genetic and environmental factors. High-throughput omics technologies are now widely available, however the integration of multi-omics data to identify viable anticancer drug-target combinations, that allow for a better clinical outcome when considering the efficacy-toxicity spectrum, is challenging. This review article provides an overview of systems approaches which help to integrate a broad spectrum of technologies and data. We focus on network approaches and investigate anticancer mechanism and biological targets of resveratrol using reverse pharmacophore mapping as an in-depth case study. The results of this case study demonstrate the use of systems approaches for a better understanding of the behavior of small molecule inhibitors in receptor binding sites. The presented network analysis approach helps in formulating hypotheses and provides mechanistic insights of resveratrol in neoplastic transformations.

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

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.

Drug–target prediction utilizing heterogeneous bio-linked network embeddings

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Yue Yu ◽  
Andrew Wen ◽  
Ming Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Association Mining ◽  
Drug Target Prediction ◽  
Specific Prediction ◽  
Series Of Experiments ◽  
Inference Methods ◽  
Novel Drug ◽  
Prediction Strategy

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

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