scholarly journals Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Qi Wang ◽  
JinCai Huang ◽  
YangHe Feng ◽  
JiaWei Fei
Keyword(s):  
Decision Trees ◽  
Drug Target ◽  
Drug Targets ◽  
Chemical Properties ◽  
Superior Performance ◽  
Target Proteins ◽  
Data Mining Algorithms ◽  
Open Access Database ◽  
Human Proteins ◽  
Physical And Chemical

The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.

Synthesis, Spectral, Bio Assay, Chromatographic - Studying of New Imidazole Reagents Via Three Components Reaction

2021 ◽  
Vol 19 (7) ◽  
pp. 115-122
Author(s):  
Mohammed Nawfal Abdul Maged Alkhafaji ◽  
Hutham Abd Ali Abd Al Hussain ◽  
Dr. Nagham Mahmood Aljamali
Keyword(s):  
Nervous System ◽  
Drug Targets ◽  
Biological Activities ◽  
Drug Carrier ◽  
Chemical Properties ◽  
Cancer Drug ◽  
Condensation Process ◽  
Chromatographic Study ◽  
The Central Nervous System ◽  
Physical And Chemical

Imidazoles are part of the theophylline Reagent, found in tea leaves and coffee beans, which stimulates the central nervous system. It is found in the anti-cancer drug mercaptopurine, which fights leukemia by interfering with DNA systems. A number of prepared imidazoles, including clotrimazole, are selective inhibitors of nitric oxide synthase, which makes them interesting drug targets in inflammation, respiratory diseases and tumors of the nervous system. Other biological activities of the drug carrier imidazole relate to deregulation of the intracellular fluxes of (Ca and K) ions. Novel imidazole –heterocyclic reagents were created via cyclization process then condensation process., followed by investigation of all created new reagents via a number of spectral performances (FT.IR, H.NMR)–spectrophotometric, other physical and chemical properties, and chromatographic study with microbial studying for all new created imidazole reagents.

scholarly journals Prioritizing and modelling of putative drug target proteins of Candida albicans by system biology approach

2018 ◽  
Vol 65 (2) ◽  
pp. 209-218 ◽  
Author(s):  
Tariq Ismail ◽  
Nighat Fatima ◽  
Syed Aun Muhammad ◽  
Syed Saoud Zaidi ◽  
Nisar Rehman ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Drug Target ◽  
Drug Targets ◽  
Cellular Localization ◽  
Critical Role ◽  
Human Microbiome ◽  
Genomic Analysis ◽  
Cellular Systems ◽  
Gut Flora ◽  
Target Proteins

Candida albicans (C. albicans) is one of the major source of nosocomial infections in human which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to affectively treat due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring completely 5090 annotated genes of Candida albicans from available databases which was categorized into essential and non-essential genes. The result indicated 9% proteins were essential that could become potential candidates for intervention which might result in pathogen death. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome as a reference to minimize the side effects. It was seen that 14% of Candidal proteins were evolutionary related to the human proteins while 86% are non-human homologs. In next step for the selection of compatible drug targets, the non-human homologs were sequentially compared to human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems exhibited that 80% are cytoplasmic, 10% are mitochondrial and remaining 10 % are associated with cell wall. The role of these non-human and non-gut flora putative target proteins in Candidal biological pathways was studied and on the basis of their integrated and critical role 4-proteins were selected for molecular modeling.  For drug designing and development, five quality and reliable protein models with more than 70% homology were constructed. Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against these infections.

scholarly journals D3Targets-2019-nCoV: A Web Server to Identify Potential Targets for Antivirals Against 2019-nCoV

2020 ◽  
Author(s):  
Yulong Shi ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
Cheng Peng ◽  
Zhengdan Zhu ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Underlying Mechanism ◽  
Modern Medicine ◽  
Potential Drug ◽  
Target Interaction ◽  
Human Proteins ◽  
Binding Pockets ◽  
Effective Drugs ◽  
Potential Drug Targets

<p>2019-nCoV has caused more than 560 deaths as of 6 February 2020 worldwide, mostly in China. Although there are no effective drugs approved, many clinical trials are incoming or ongoing in China which utilize traditional chinese medicine or modern medicine. Moreover, many groups are working on the cytopathic effect assay to fight against 2019-nCoV, which will result in compounds with good activity yet unknown targets. Identifying potential drug targets will be of great importance to understand the underlying mechanism of how the drug works. Here, we <a></a><a>compiled</a> the 3D structures of 17 2019-nCoV proteins and 3 related human proteins, which resulted in 208 binding pockets. Each submitted compound will be docked to these binding pockets by the docking software smina and the docking results will be presented in ascending order of compound-target interaction energy (kcal/mol). We hope the computational tool will shed some light on the potential drug target for the identified antivirals. D3Targets-2019-nCoV is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Docking/index.php.</p>

scholarly journals Progress in the Application of Carbon Dots-Based Nanozymes

2021 ◽  
Vol 9 ◽  
Author(s):  
Jun Jin ◽  
Linlin Li ◽  
Lihui Zhang ◽  
Zhihui Luan ◽  
Shuquan Xin ◽  
...  
Keyword(s):  
Carbon Dots ◽  
Surface Engineering ◽  
Chemical Properties ◽  
Superior Performance ◽  
Advantages And Disadvantages ◽  
Stable Chemical ◽  
Chemical Inertness ◽  
Superior Surface ◽  
Working Principle ◽  
Physical And Chemical

As functional nanomaterials with simulating enzyme-like properties, nanozymes can not only overcome the inherent limitations of natural enzymes in terms of stability and preparation cost but also possess design, versatility, maneuverability, and applicability of nanomaterials. Therefore, they can be combined with other materials to form composite nanomaterials with superior performance, which has garnered considerable attention. Carbon dots (CDs) are an ideal choice for these composite materials due to their unique physical and chemical properties, such as excellent water dispersion, stable chemical inertness, high photobleaching resistance, and superior surface engineering. With the continuous emergence of various CDs-based nanozymes, it is vital to thoroughly understand their working principle, performance evaluation, and application scope. This review comprehensively discusses the recent advantages and disadvantages of CDs-based nanozymes in biomedicine, catalysis, sensing, detection aspects. It is expected to provide valuable insights into developing novel CDs-based nanozymes.

scholarly journals Detecting Drug-Target Binding in Cells using Fluorescence Activated Cell Sorting Coupled with Mass Spectrometry Analysis

2017 ◽  
Author(s):  
Kris Wilson ◽  
Scott P Webster ◽  
John P Iredale ◽  
Xiaozhong Zheng ◽  
Natalie Z Homer ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Mass Spectrometry Analysis ◽  
Protein Targets ◽  
Target Proteins ◽  
Spectrometry Analysis ◽  
Cellular Permeability ◽  
Target Binding ◽  
Hit Validation

AbstractThe assessment of drug-target engagement for determining the efficacy of a compound inside cells remains challenging, particularly for difficult target proteins. Existing techniques are more suited to soluble protein targets. Difficult target proteins include those with challenging in vitro solubility, stability or purification properties that preclude target isolation. Here, we report a novel technique that measures intracellular compound-target complex formation, as well as cellular permeability, specificity and cytotoxicity - the Toxicity-Affinity-Permeability-Selectivity (TAPS) technique. The TAPS assay is exemplified here using human kynurenine 3-monooxygenase (KMO), a challenging intracellular membrane protein target of significant current interest. TAPS confirmed target binding of known KMO inhibitors inside cells. We conclude that the TAPS assay can be used to facilitate intracellular hit validation on most, if not all intracellular drug targets.

scholarly journals D3Targets-2019-nCoV: A Web Server to Identify Potential Targets for Antivirals Against 2019-nCoV

2020 ◽  
Author(s):  
Yulong Shi ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
Cheng Peng ◽  
Zhengdan Zhu ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Underlying Mechanism ◽  
Modern Medicine ◽  
Potential Drug ◽  
Target Interaction ◽  
Human Proteins ◽  
Binding Pockets ◽  
Effective Drugs ◽  
Potential Drug Targets

<p>2019-nCoV has caused more than 560 deaths as of 6 February 2020 worldwide, mostly in China. Although there are no effective drugs approved, many clinical trials are incoming or ongoing in China which utilize traditional chinese medicine or modern medicine. Moreover, many groups are working on the cytopathic effect assay to fight against 2019-nCoV, which will result in compounds with good activity yet unknown targets. Identifying potential drug targets will be of great importance to understand the underlying mechanism of how the drug works. Here, we <a></a><a>compiled</a> the 3D structures of 17 2019-nCoV proteins and 3 related human proteins, which resulted in 208 binding pockets. Each submitted compound will be docked to these binding pockets by the docking software smina and the docking results will be presented in ascending order of compound-target interaction energy (kcal/mol). We hope the computational tool will shed some light on the potential drug target for the identified antivirals. D3Targets-2019-nCoV is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Docking/index.php.</p>

scholarly journals Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

2019 ◽  
Vol 35 (16) ◽  
pp. 2818-2826 ◽  
Author(s):  
Jinyan Chan ◽  
Xuan Wang ◽  
Jacob A Turner ◽  
Nicole E Baldwin ◽  
Jinghua Gu
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Effective Means ◽  
Drug Repurposing ◽  
Superior Performance ◽  
Supplementary Information ◽  
Breast Cancer Dataset ◽  
Specific Drug ◽  
Cancer Dataset ◽  
Disease Specific

Abstract Motivation Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. Availability and implementation Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TarDict: A RandomForestClassifier based software predicts drug-target interaction using SMILES

2021 ◽  
pp. bi202101
Author(s):  
Peter Habib ◽  
Alsamman Alsamman ◽  
Sameh Hassanein ◽  
Aladdin Hamwieh
Keyword(s):  
Machine Learning ◽  
Computational Model ◽  
Drug Target ◽  
Drug Targets ◽  
Cost Effective ◽  
New Drugs ◽  
Drug Target Discovery ◽  
Target Proteins ◽  
Biological Targets ◽  
Testing Dataset

The future of therapeutics depends on understanding the interaction between the chemical structure of the drug and the target protein that contributes to the etiology of the disease in order to improve drug discovery. Predicting the target of unknown drugs being investigated from already identified drug data is very important not only for understanding different processes of drug and molecular interactions but also for the development of new drugs. Using machine learning and published drug information we design an easy-to-use tool that predicts biological target proteins for medical drugs. TarDict is based on a chemical-simplified line-entry molecular input system called SMILES. It receives SMILES entries and returns a list of possible similar drugs as well as possible drug-targets. TarDict uses 20442 drug entries that have well-known biological targets to construct a prognostic computational model capable of predicting novel drug targets with an accuracy of 95%. We developed a machine learning approach to recommend target proteins to approved drug targets. We have shown that the proposed method is highly predictive on a testing dataset consisting of 4088 targets and 102 manually entered drugs. The proposed computational model is an efficient and cost-effective tool for drug target discovery and prioritization. Such novel tool could be used to enhance drug design, predict potential target and identify combination therapy crossroads.

scholarly journals A hidden human proteome encoded by ‘non-coding’ genes

2019 ◽  
Vol 47 (15) ◽  
pp. 8111-8125 ◽  
Author(s):  
Shaohua Lu ◽  
Jing Zhang ◽  
Xinlei Lian ◽  
Li Sun ◽  
Kun Meng ◽  
...  
Keyword(s):  
Multiple Reaction Monitoring ◽  
Chemical Properties ◽  
Shotgun Proteomics ◽  
Ribosome Profiling ◽  
Translation Efficiency ◽  
Reaction Monitoring ◽  
Translation Elongation ◽  
Protein Functions ◽  
Human Proteins ◽  
Physical And Chemical

Abstract It has been a long debate whether the 98% ‘non-coding’ fraction of human genome can encode functional proteins besides short peptides. With full-length translating mRNA sequencing and ribosome profiling, we found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes with active translation elongation. With shotgun proteomics, 308 lncRNA-encoded new proteins were detected. A total of 207 unique peptides of these new proteins were verified by multiple reaction monitoring (MRM) and/or parallel reaction monitoring (PRM); and 10 new proteins were verified by immunoblotting. We found that these new proteins deviated from the canonical proteins with various physical and chemical properties, and emerged mostly in primates during evolution. We further deduced the protein functions by the assays of translation efficiency, RNA folding and intracellular localizations. As the new protein UBAP1-AST6 is localized in the nucleoli and is preferentially expressed by lung cancer cell lines, we biologically verified that it has a function associated with cell proliferation. In sum, we experimentally evidenced a hidden human functional proteome encoded by purported lncRNAs, suggesting a resource for annotating new human proteins.

Statistical criteria of stellar population types

1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska
Keyword(s):  
Stellar Population ◽  
Chemical Properties ◽  
Spectrophotometric Study ◽  
Physical And Chemical Properties ◽  
Population Type ◽  
The Galaxy ◽  
Distribution Parameters ◽  
Physical And Chemical ◽  
Statistical Criteria ◽  
And Chemical Properties

In connection with the spectrophotometric study of population-type characteristics of various kinds of stars, a statistical analysis of kinematical and distribution parameters of the same stars is performed at the Toruń Observatory. This has a twofold purpose: first, to provide a practical guide in selecting stars for observing programmes, second, to contribute to the understanding of relations existing between the physical and chemical properties of stars and their kinematics and distribution in the Galaxy.

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