scholarly journals Drug Target Prediction Based on the Herbs Components: The Study on the Multitargets Pharmacological Mechanism of Qishenkeli Acting on the Coronary Heart Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Wang ◽  
Zhongyang Liu ◽  
Chun Li ◽  
Dong Li ◽  
Yulin Ouyang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Angiotensin Ii ◽  
Drug Target ◽  
Drug Targets ◽  
Target Prediction ◽  
Coronary Artery Ligation ◽  
Potential Drug ◽  
Drug Target Prediction ◽  
Potential Drug Targets

In this paper, we present a case study of Qishenkeli (QSKL) to research TCM’s underlying molecular mechanism, based on drug target prediction and analyses of TCM chemical components and following experimental validation. First, after determining the compositive compounds of QSKL, we use drugCIPHER-CS to predict their potential drug targets. These potential targets are significantly enriched with known cardiovascular disease-related drug targets. Then we find these potential drug targets are significantly enriched in the biological processes of neuroactive ligand-receptor interaction, aminoacyl-tRNA biosynthesis, calcium signaling pathway, glycine, serine and threonine metabolism, and renin-angiotensin system (RAAS), and so on. Then, animal model of coronary heart disease (CHD) induced by left anterior descending coronary artery ligation is applied to validate predicted pathway. RAAS pathway is selected as an example, and the results show that QSKL has effect on both rennin and angiotensin II receptor (AT1R), which eventually down regulates the angiotensin II (AngII). Bioinformatics combing with experiment verification can provide a credible and objective method to understand the complicated multitargets mechanism for Chinese herbal formula.

scholarly journals Interpretable Drug Target Predictions using Self-Expressiveness

2021 ◽  
Author(s):  
Diego Galeano ◽  
Santiago Noto ◽  
Ruben Jimenez ◽  
Alberto Paccanaro
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Prediction Models ◽  
Matrix Completion ◽  
Closed Form Solution ◽  
Target Prediction ◽  
Form Solution ◽  
Protein Targets ◽  
Drug Target Prediction ◽  
Similarity Matrices

AbstractThe identification of missing drug targets is critical for the development of treatments and for the molecular elucidation of drug side effects. Drug targets have been predicted by exploiting molecular, biological or pharmacological features of drugs and protein targets. Yet, developing integrative and interpretable machine learning models for predicting drug targets remains a challenging task. We present Inception, an integrative and interpretable matrix completion model for predicting drug targets. Inception is a self-expressive model that learns two similarity matrices: one for drugs and another for protein targets. These learned similarity matrices are key for our models’ interpretability: they can explain how a predicted drug-target interaction can be explain in terms of a linear combination of chemical, biological and pharmacological similarities. We develop a novel objective function with efficient closed-form solution. To demonstrate the ability of Inception at recovering missing drug-target interactions (DTIs), we perform cross-validation experiments with stringent controls of data imbalance, chemical similarities between drugs and sequence similarities between targets. We also assess the performance of our model using a simulated prospective approach. Having trained our model with DTIs from a snapshot 2011 of the DrugBank database, we test whether we could predict DTIs from a 2020 snapshot of DrugBank. Inception outperforms two state-of-the-art drug target prediction models in all the scenarios. This suggests that Inception could be useful for predicting missing drug target interactions while providing interpretable predictions.

scholarly journals Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
María Gordillo-Marañón ◽  
Magdalena Zwierzyna ◽  
Pimphen Charoen ◽  
Fotios Drenos ◽  
Sandesh Chopade ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Drug Target ◽  
Drug Targets ◽  
Human Genetics ◽  
Expression Profiles ◽  
Gene Encoding ◽  
Beneficial Effects ◽  
Independent Replication ◽  
Protein Expression Profiles

AbstractDrug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target’s expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process.

scholarly journals Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

2020 ◽  
Vol 36 (16) ◽  
pp. 4490-4497
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Supplementary Information ◽  
Training Scheme ◽  
Drug Target Prediction

Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

2021 ◽  
Author(s):  
Maria Gordillo-Maranon ◽  
Magdalena Zwierzyna ◽  
Pimphen Charoen ◽  
Fotios Drenos ◽  
Sandesh Chopade ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Drug Target ◽  
Drug Targets ◽  
Human Genetics ◽  
Expression Profiles ◽  
Gene Encoding ◽  
Human Protein Atlas ◽  
Dna Sequence Variants ◽  
Protein Expression Profiles

Abstract Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter its expression or function, as a tool to anticipate the effect of drug action on the same target. Here, we applied MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets were further prioritized using genetic co-localization, protein expression profiles from the Human Protein Atlas and, for targets with a licensed drug or an agent in clinical development, by sourcing data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with circulating blood lipids (HDL-C, LDL-C and triglycerides), we were able to robustly prioritize 30 targets that might elicit beneficial treatment effects in the prevention or treatment of CHD. The prioritized list included NPC1L1 and PCSK9, the targets of licensed drugs whose efficacy has been already proven in clinical trials. To conclude, we discuss how this approach can be generalized to other targets, disease biomarkers and clinical end-points to help prioritize and validate targets during the drug development process.

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 Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 127
Author(s):  
Gurudeeban Selvaraj ◽  
Satyavani Kaliamurthi ◽  
Gilles H. Peslherbe ◽  
Dong-Qing Wei
Keyword(s):  
Signaling Pathways ◽  
Drug Target ◽  
Drug Targets ◽  
Structural Modeling ◽  
Protein Docking ◽  
Receptor Signaling ◽  
Potential Drug ◽  
Ppi Network ◽  
Candidate Drug ◽  
Potential Drug Targets

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

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 Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

2017 ◽  
Author(s):  
Gokmen Altay ◽  
Elmar Nurmemmedov ◽  
Santosh Kesari ◽  
David E. Neal
Keyword(s):  
Prostate Cancer ◽  
Software Package ◽  
Drug Targets ◽  
Target Prediction ◽  
Potential Drug ◽  
Disease Mechanism ◽  
R Software ◽  
Cancer Dataset ◽  
Link Type ◽  
Potential Drug Targets

AbstractWe present an R software package that performs at genome-wide level differential network analysis and infers only disease-specific molecular interactions between two different cell conditions. This helps revealing the disease mechanism and predicting most influential genes as potential drug targets or biomarkers of the disease condition of interest. As an exemplary analysis, we performed an application of the software over LNCaP datasets and, out of approximately 25000 genes, predicted CXCR7 and CXCR4 together as drug targets of LNCaP prostate cancer dataset. We further successfully validated them with our initial wet-lab experiments. The introduced software can be applied to all the diseases, especially cancer, with gene expression data of two different conditions (e.g. tumor vs normal) and thus has the potential of a global benefit. As a distinct remark, our software provide the causal disease mechanism with multiple potential drug-targets rather than a single independent target prediction.AvailabilityThe introduced R software package for the analysis is available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net

scholarly journals Comparative Genome and Network Centrality Analysis to Identify Drug Targets ofMycobacterium tuberculosis H37Rv

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Tilahun Melak ◽  
Sunita Gakkhar
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Drug Targets ◽  
Tuberculosis H37rv ◽  
Network Centrality ◽  
Potential Drug ◽  
Comparative Genome ◽  
Mycobacterium Tuberculosis H37rv ◽  
Centrality Analysis ◽  
Potential Drug Targets

Potential drug targets ofMycobacterium tuberculosis H37Rvwere identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome ofMycobacterium tuberculosis H37Rvagainst Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

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