Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs’ targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

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Bioinformatics studies on structures, functions and diversifications of rolling leaf related genes in rice (Oryza sativa L.)

Plant Genetic Resources ◽

10.1017/s1479262120000404 ◽

2020 ◽

pp. 1-14

Author(s):

Md. Jahangir Alam ◽

Md. Alamin ◽

Most. Humaira Sultana ◽

Md. Asif Ahsan ◽

Md. Ripter Hossain ◽

...

Keyword(s):

Protein Interactions ◽

Molecular Mechanisms ◽

Oryza Sativa L ◽

Vital Role ◽

High Yield ◽

Leaf Rolling ◽

Protein Protein Interactions ◽

Conserved Domains ◽

Kegg Pathways ◽

Conserved Domain

Abstract Leaf morphology of crop plants has significant value in agronomy. Leaf rolling in rice plays a vital role to increase grain yield. However, collective information on the rolling leaf (RL) genes reported to date and different comparative bioinformatics studies of their sequences are still incomplete. This bioinformatics study was designed to investigate structures, functions and diversifications of the RL related genes reported till now through several studies. We performed different comparative and functional analyses of the selected 42 RL genes among 103 RL genes using different bioinformatics techniques including gene structure, conserved domain, phylogenetic, gene ontology (GO), transcription factor (TF), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein–protein network. Exon-intron organization and conserved domain analysis showed diversity in structures and conserved domains of RL genes. Phylogenetic analysis classified the proteins into five major groups. GO and TF analyses revealed that regulation-related genes were remarkably enriched in biological process and 10 different TF families were involved in rice leaf rolling. KEGG analysis demonstrated that 14 RL genes were involved in the KEGG pathways, among which 50% were involved in the metabolism pathways. Of the selected RL proteins, 55% proteins were non-interacting with other RL proteins and OsRL9 was the most interacting RL protein. These results provide important information regarding structures, conserved domains, phylogenetic revolution, protein–protein interactions and other genetic bases of RL genes which might be helpful to the researchers for functional analysis of new candidate RL genes to explore their characteristics and molecular mechanisms for high yield rice breeding.

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Prediction of Drug Combinations with a Network Embedding Method

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181226170140 ◽

2019 ◽

Vol 21 (10) ◽

pp. 789-797 ◽

Cited By ~ 11

Author(s):

Tianyun Wang ◽

Lei Chen ◽

Xian Zhao

Keyword(s):

Computational Methods ◽

Drug Combination ◽

Chemical Interaction ◽

Drug Combinations ◽

Computational Techniques ◽

Svm Classifier ◽

Network Embedding ◽

Embedding Method ◽

Topological Features ◽

Individual Features

Aim and Objective: There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way. Method: In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier. Results and Conclusion: The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

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Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

Biology ◽

10.3390/biology9090278 ◽

2020 ◽

Vol 9 (9) ◽

pp. 278

Author(s):

Jin Li ◽

Yang Huo ◽

Xue Wu ◽

Enze Liu ◽

Zhi Zeng ◽

...

Keyword(s):

Cell Lines ◽

Drug Combination ◽

Computational Models ◽

Target Genes ◽

Statistical Significance ◽

Drug Combinations ◽

Cancer Therapies ◽

Gene Expressions ◽

Association Analyses ◽

Kegg Pathways

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

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Quantitative systems pharmacological analysis of drugs of abuse reveals the pleiotropy of their targets and the effector role of mTORC1

10.1101/470922 ◽

2018 ◽

Author(s):

Fen Pei ◽

Hongchun Li ◽

Bing Liu ◽

Ivet Bahar

Keyword(s):

Drug Addiction ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Drugs Of Abuse ◽

Morphological Changes ◽

Protein Protein Interactions ◽

Kegg Pathways ◽

Synaptic Neurotransmission ◽

Early Effects

AbstractExisting treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as a common upstream signaling module that ‘senses’ the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter is proposed to act as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

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Discovering Synergistic Drug Combination from a Computational Perspective

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666180330141804 ◽

2018 ◽

Vol 18 (12) ◽

pp. 965-974 ◽

Cited By ~ 3

Author(s):

Pingjian Ding ◽

Jiawei Luo ◽

Cheng Liang ◽

Qiu Xiao ◽

Buwen Cao ◽

...

Keyword(s):

Computational Methods ◽

Drug Combination ◽

Computational Models ◽

Rapid Development ◽

Drug Combinations ◽

Effective Drug ◽

In Vitro Method ◽

Feature Based ◽

Synergistic Drug Combinations

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.

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TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

BioMed Research International ◽

10.1155/2020/4625123 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Qiannan Yang ◽

Bojun Yu ◽

Jing Sun

Keyword(s):

Gene Expression ◽

Endometrial Cancer ◽

Protein Interactions ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Kegg Pathways ◽

Kaplan Meier ◽

Pathway Analyses

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.

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Combination of Hydroxychloroquine, Melatonin and Mercaptopurine as possible intervention for prophylaxis and treatment of Novel COVID-19 infection

Coronaviruses ◽

10.2174/2666796701999201016154957 ◽

2020 ◽

Vol 01 ◽

Author(s):

Gyan Vardhan ◽

Vikas Kumar ◽

Megha Agrawal ◽

Puneet Dhamija

Keyword(s):

Mechanism Of Action ◽

Drug Combination ◽

Treatment Strategies ◽

Effective Drug ◽

Effective Alternative ◽

Potential Candidate ◽

Potential Drug ◽

Review Of Literature ◽

Scientific Review ◽

Repurposed Drugs

Background: COVID-19 has been declared as a pandemic recently and has caused many deaths worldwide. Till date no effective drug or vaccine is available against SARS-CoV-2. There is an urgent need to find effective alternative preventive and treatment strategies to deal with SARS-CoV-2 outbreak. Objective: This communication proposes a new potential drug combination (repurposed) for prophylaxis and treatment of SARS-CoV-2. Methods and Materials: We performed a brief review of literature on combination of Hydroxychloroquine, Melatonin and Mercaptopurine for prophylaxis and treatment of Novel COVID-19 infection and also assessed their possible mechanism of action against SARS-CoV-2. Observation: Proposed combination seems to be safe and target is unlikely to develop resistance to this combination. Conclusion: This scientific review proposes potential repurposed drugs and their combination targeting SARS-CoV-2. Conclusion: This scientific review proposes potential candidate repurposed drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

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Adhesion and Growth of Neuralized Mouse Embryonic Stem Cells on Parylene-C/SiO2 Substrates

Materials ◽

10.3390/ma14123174 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3174

Author(s):

Alan F. Murray ◽

Evangelos Delivopoulos

Keyword(s):

Stem Cells ◽

Embryonic Stem Cells ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Embryonic Stem ◽

Mouse Embryonic Stem Cells ◽

Primary Neurons ◽

Tissue Engineering Scaffolds ◽

Parylene C ◽

Unexpected Outcome

Neuronal patterning on microfabricated architectures has developed rapidly over the past few years, together with the emergence of soft biocompatible materials and tissue engineering scaffolds. Previously, we introduced a patterning technique based on serum and the biopolymer parylene-C, achieving highly compliant growth of primary neurons and astrocytes on different geometries. Here, we expanded this technique and illustrated that neuralized cells derived from mouse embryonic stem cells (mESCs) followed stripes of variable widths with conformity equal to or higher than that of primary neurons and astrocytes. Our results indicate the presence of undifferentiated mESCs, which also conformed to the underlying patterns to a high degree. This is an exciting and unexpected outcome, as molecular mechanisms governing cell and ECM protein interactions are different in stem cells and primary cells. Our study enables further investigations into the development and electrophysiology of differentiating patterned neural stem cells.

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Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

Current Medicinal Chemistry ◽

10.2174/0929867324666170426095015 ◽

2018 ◽

Vol 25 (1) ◽

pp. 5-21 ◽

Cited By ~ 25

Author(s):

Ylenia Cau ◽

Daniela Valensin ◽

Mattia Mori ◽

Sara Draghi ◽

Maurizio Botta

Keyword(s):

Protein Interactions ◽

Molecular Mechanisms ◽

Physiological Role ◽

Specific Protein ◽

Protein Protein Interactions ◽

Cellular Processes ◽

Protein Partners ◽

High Sequence Homology ◽

Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

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Biomolecular Network-Based Synergistic Drug Combination Discovery

BioMed Research International ◽

10.1155/2016/8518945 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Xiangyi Li ◽

Guangrong Qin ◽

Qingmin Yang ◽

Lanming Chen ◽

Lu Xie

Keyword(s):

Drug Combination ◽

Complex Disease ◽

Recent Decade ◽

Drug Combinations ◽

Network Medicine ◽

Disease Therapy ◽

Biomolecular Network ◽

Disease Related Genes ◽

Disease Specific ◽

Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

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