scholarly journals Application of Centrality Measures for Potential Drug Targets: Review

2020 ◽  
Vol 9 (04) ◽  
pp. 24989-24993
Author(s):  
Akula Chandra Sekhar ◽  
Ch. Ambedkar
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Crucial Role ◽  
Drug Targets ◽  
Docking Studies ◽  
Drug Binding ◽  
Centrality Measures ◽  
Potential Drug ◽  
Protein Protein Interactions ◽  
Potential Drug Targets

Protein-Protein Interactions (PPI) have important role in drug binding with the Proteins called drug targets. For identifying the potential drug targets there are different techniques. In this paper we are presenting application of Centrality Measures for identifying the drug targets. Centrality measure indicates importance of node in the graph or network. Protein-Protein Interactions for proteins which are involved in a particular disease are identified and centrality measures will be calculated based on the graph built suing the PPI interactions. Further the nodes which are playing crucial role will be identified using the various centrality measures and these drug targets can be used for drug discovery of a particular disease through insilico docking studies.

Pseudokinase drug intervention: a potentially poisoned chalice

2013 ◽  
Vol 41 (4) ◽  
pp. 1083-1088 ◽  
Author(s):  
Jeroen Claus ◽  
Angus J.M. Cameron ◽  
Peter J. Parker
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Binding Pocket ◽  
Atp Binding ◽  
Potential Drug ◽  
Protein Protein Interactions ◽  
Drug Intervention ◽  
Potential Impact ◽  
Potential Drug Targets ◽  
Selection Of

Pseudokinases, the catalytically impaired component of the kinome, have recently been found to share more properties with active kinases than previously thought. In many pseudokinases, ATP binding and even some activity is preserved, highlighting these proteins as potential drug targets. In both active kinases and pseudokinases, binding of ATP or drugs in the nucleotide-binding pocket can stabilize specific conformations required for activity and protein–protein interactions. We discuss the implications of locking particular conformations in a selection of (pseudo)kinases and the dual potential impact on the druggability of these proteins.

A Comparative Computational Analysis Approach to Predict Significant Protein-Protein Interactions of Human and Vancomycin Resistant Enterococcus faecalis (VRE) to Prioritize Potential Drug Targets

2021 ◽  
Vol 18 ◽  
Author(s):  
Reaz Uddin ◽  
Kanwal Khan
Keyword(s):  
Enterococcus Faecalis ◽  
Protein Interactions ◽  
Drug Targets ◽  
Vancomycin Resistant Enterococcus ◽  
Potential Drug ◽  
Protein Protein Interactions ◽  
Clp Protease ◽  
Vancomycin Resistant ◽  
Novel Drug ◽  
Potential Drug Targets

Background: Various challenges exist in the treatment of infectious diseases due to the significant rise in drug resistance, resulting in the failure of antibiotic treatment. As a consequence, a dire need has arisen for the rethinking of the drug discovery cycle because of the challenge of drug resistance. The underlying cause of the infectious diseases depends upon associations within the Host-pathogen Protein-Protein Interactions (HP-PPIs) network, which represents a key to unlock new pathogenesis mechanism. Hence, the elucidation of significant PPIs is a promising approach for the identification of potential drug targets. Objective: Identification of the most significant HP-PPIs and their partners, and target them to prioritize potential new drug targets against Vancomycin-resistant Enterococcus faecalis (VRE). Methods: We applied a computational approach based on one of the emerging techniques i.e. Interolog methodology to predict the significant Host-Pathogen PPIs. Structure-Based Studies were applied to model shortlisted protein structures and validate them through PSIPRED, PROCHECK, VERIFY3D, and ERRAT tools. Furthermore, 18,000 drug-like compounds from the ZINC library were docked against these proteins to study protein-chemical interactions using the AutoDock based molecular docking method. Results: Study resulted in the identification of 118 PPIs for Enterococcus faecalis, and prioritized two novel drug targets i.e. Exodeoxyribonuclease (ExoA) and ATP-dependent Clp protease proteolytic subunit (ClpP). Consequently, the docking program ranked 2,670 and 3,154 compounds as potential binders against Exodeoxyribonuclease and ATP-dependent Clp protease proteolytic subunit, respectively. Conclusion: Thereby, the current study enabled us to identify and prioritize potential PPIs in VRE and their interacting proteins in human hosts along with the pool of novel drug candidates.

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.

Using a Heat Diffusion Model to Detect Potential Drug Resistance Genes of Mycobacterium tuberculosis

2020 ◽  
Vol 27 (8) ◽  
pp. 711-717 ◽  
Author(s):  
Ze-Jia Cui ◽  
Wei-Tong Zhang ◽  
Qiang Zhu ◽  
Qing-Ye Zhang ◽  
Hong-Yu Zhang
Keyword(s):  
Molecular Dynamics ◽  
Drug Resistance ◽  
Diffusion Model ◽  
Protein Interactions ◽  
Drug Targets ◽  
Sequence Data ◽  
Heat Diffusion ◽  
Dynamics Simulation ◽  
Potential Drug ◽  
Protein Protein Interactions

Background: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is one of the oldest known and most dangerous diseases. Although the spread of TB was controlled in the early 20th century using antibiotics and vaccines, TB has again become a threat because of increased drug resistance. There is still a lack of effective treatment regimens for a person who is already infected with multidrug-resistant Mtb (MDR-Mtb) or extensively drug-resistant Mtb (XDRMtb). In the past decades, many research groups have explored the drug resistance profiles of Mtb based on sequence data by GWAS, which identified some mutations that were significantly linked with drug resistance, and attempted to explain the resistance mechanisms. However, they mainly focused on several significant mutations in drug targets (e.g. rpoB, katG). Some genes which are potentially associated with drug resistance may be overlooked by the GWAS analysis. Objective: In this article, our motivation is to detect potential drug resistance genes of Mtb using a heat diffusion model. Methods: All sequencing data, which contained 127 samples of Mtb, i.e. 34 ethambutol-, 65 isoniazid-, 53 rifampicin- and 45 streptomycin-resistant strains. The raw sequence data were preprocessed using Trimmomatic software and aligned to the Mtb H37Rv reference genome using Bowtie2. From the resulting alignments, SAMtools and VarScan were used to filter sequences and call SNPs. The GWAS was performed by the PLINK package to obtain the significant SNPs, which were mapped to genes. The P-values of genes calculated by GWAS were transferred into a heat vector. The heat vector and the Mtb protein-protein interactions (PPI) derived from the STRING database were inputted into the heat diffusion model to obtain significant subnetworks by HotNet2. Finally, the most significant (P < 0.05) subnetworks associated with different phenotypes were obtained. To verify the change of binding energy between the drug and target before and after mutation, the method of molecular dynamics simulation was performed using the AMBER software. Results: We identified significant subnetworks in rifampicin-resistant samples. Excitingly, we found rpoB and rpoC, which are drug targets of rifampicin. From the protein structure of rpoB, the mutation location was extremely close to the drug binding site, with a distance of only 3.97 Å. Molecular dynamics simulation revealed that the binding energy of rpoB and rifampicin decreased after D435V mutation. To a large extent, this mutation can influence the affinity of drug-target binding. In addition, topA and pyrG were reported to be linked with drug resistance, and might be new TB drug targets. Other genes that have not yet been reported are worth further study. Conclusion: Using a heat diffusion model in combination with GWAS results and protein-protein interactions, the significantly mutated subnetworks in rifampicin-resistant samples were found. The subnetwork not only contained the known targets of rifampicin (rpoB, rpoC), but also included topA and pyrG, which are potentially associated with drug resistance. Together, these results offer deeper insights into drug resistance of Mtb, and provides potential drug targets for finding new antituberculosis drugs.

scholarly journals New machine learning and physics-based scoring functions for drug discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Isabella A. Guedes ◽  
André M. S. Barreto ◽  
Diogo Marinho ◽  
Eduardo Krempser ◽  
Mélaine A. Kuenemann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Support Vector ◽  
Scoring Functions ◽  
Protein Protein Interactions ◽  
Energy Prediction ◽  
Original Class

AbstractScoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

scholarly journals Identification of Potential Drug Targets and Prediction of the Potential Impact of High Risk Non Synonymous Single Nucleotide Polymorphism in SARS-CoV-2 3C like Proteinase (3CLpro): A Computational Approach

2020 ◽  
Author(s):  
Sahar Elbager ◽  
abdelrahman hamza ◽  
Afra M. Al Bkrye ◽  
Asia M. Alrashied ◽  
Entisar N. M. Ali ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Drug Binding ◽  
Snp Analysis ◽  
Potential Drug ◽  
Full Genome Sequencing ◽  
Drug Binding Site ◽  
Adme Properties ◽  
Approved Drugs ◽  
And Function

On January 2020, a new coronavirus (officially named SARS-CoV-2) was associated with alarming outbreak of a pneumonia-like illness, which was later named by the WHO as COVID-19, originating from Wuhan City, China. Although many clinical studies involving antiviral and immunomodulatory drug treatments for SARS-CoV-2 all without reported results, no approved drugs have been found to effectively inhibit the virus so far. Full genome sequencing of the virus was done, and uploaded to be freely available for the world scientists to explore. A promising target for SARS-CoV-2 drug design is a chymotrypsin-like cysteine protease (3CLpro), a main protease responsible for the replication and maturation of functional proteins in the life cycle of the SARS coronavirus. Here we aim to explore SARS-CoV-2 3CLpro as possible drug targets based on ligand- protein interactions. In addition, ADME properties of the ligands were also analyzed to predict their drug likeliness. The results revealed Out of 9 ligands, 8 ligands (JFM, X77, RZG, HWH, T8A, 0EN, PEPTIDE and DMS) showed best ADME properties. These findings suggest that these ligands can be used as potential molecules for developing potent inhibitors against SARS-CoV-2 3CLpro, which could be helpful in inhibiting the propagation of the COVID-19. Furthermore, 10 potential amino acids residues were recognized as potential drug binding site (THR25, HIS41, GLY143, SER144, CYS145, MET165, GLU166, GLN189, ASP295 and ARG298). All those amino acid residues were subjected to missense SNP analysis were recognized to affect the structure and function of the protein. These characteristics provide them the promising to be target sites for the fresh generation inhibitors to work with and overcome drug resistance. These findings would be beneficial for the drug development for inhibiting SARS-CoV-2 3CLpro hence assisting the pharmacogenomics effort to manage the infection. of SARS-CoV-2.

scholarly journals New directions for drug discovery in psychiatric disease

2002 ◽  
Vol 4 (4) ◽  
pp. 336-341
Keyword(s):  
Animal Model ◽  
Drug Discovery ◽  
Animal Models ◽  
Drug Targets ◽  
Disease Process ◽  
Psychiatric Disease ◽  
Potential Drug ◽  
New Directions ◽  
Potential Drug Targets

Although many new potential drug targets have been discovered subsequent to the cloning of the human genome and the discovery of most of the relevant receptors, the role of these receptors in psychiatric disease is still not clear. We argue that research into the disease process leading to new animal models that can be transposed to man is critical to drug discovery, and present an example of an animal model for schizophrenia using electroencephalography.

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