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Analyzing the protein-protein interaction network and the topological properties of prostate cancer and allied diseases: A computational bioinformatics approach

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GENE REPORTS 2020年 21卷

作者： Puspo, Nadira Akter Akter, Laboni Siddique, Sinthia Paul, Bikash Kumar Ahmed, Kawsar Bhuiyan, Touhid Islam, Md Kabirul Daffodil Int Univ Dept Software Engn Dhaka Bangladesh Mawlana Bhashani Sci & Technol Univ MBSTU Dept Informat & Commun Technol ICT Santosh 1902 Tangail Bangladesh Mawlana Bhashani Sci & Technol Univ MBSTU Grp Biophotomatix Santosh 1902 Tangail Bangladesh Daffodil Int Univ Dept Multimedia & Creat Technol Dhaka Bangladesh

Background and objectives: Some of cancer diseases are related to each other by their metabolic structures. Literature reviews show that Prostate Cancer (PC), Breast Cancer (BC), Bladder Cancer (BDC) and Colorectal Cancer (CRC) are related. Some are shown up for affected family background in their early or grown-up age. Materials and methods: Python programming language is used for data mining, pre-processing and sorting and finding common genes from gathered data whose are collected National Centre of Biotechnology Information (NCBI). Protein-Protein Interaction (PPIs) and Protein Disease Interaction (PDI) are displayed by using bioinformatics technology. We use identified hub genes for making co-expression and physical interaction. Results: Interactions for selected top 8 genes are exhibited following different bioinformatics tools. The genemiRNA interaction generates interactions with a total of 651 links between 8 genes. Where, the TF-gene Interaction creates relationships between 176 nodes and 278 edges. There are 6 seed nodes. Besides, PDI represents a subnetwork which creates relationships between 47 nodes and 46 edges. There are 1 seed nodes. In addition, PCI creates relationships between 1437 nodes and 2165 edges. There are 7 seed nodes. Furthermore, GDA creates relationships between 235 nodes and 272 edges. There are 5 seed nodes. Conclusion: This study will be helpful for further studies of different bioinformatics tools for designing gene network models and drugs design. These drugs can be considered for further verification by chemical experiments.

关键词： computational bioinformatics Prostate cancer (PC) Breast cancer (BC) Bladder cancer (BDC) Colorectal cancer (CRC) Protein-protein interaction network

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Molecular simulations reveal intricate coupling between agonist-bound R-adrenergic receptors and G protein

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ISCIENCE 2025年第2期28卷 111741页

作者： Han, Yanxiao Dawson, John R. D. Demarco, Kevin R. Rouen, Kyle C. Ngo, Khoa Bekker, Slava Yarov-Yarovoy, Vladimir Clancy, Colleen E. Xiang, Yang K. Ahn, Surl-Hee Vorobyov, Igor Univ Calif Davis Dept Physiol & Membrane Biol Davis CA 95616 USA Univ Calif Davis Biophys Grad Grp Davis CA 95616 USA Amer River Coll Sacramento CA 95841 USA Univ Calif Davis Dept Anesthesiol & Pain Med Davis CA 95616 USA Univ Calif Davis Ctr Precis Med & Data Sci Davis CA 95616 USA Univ Calif Davis Dept Pharmacol Davis CA 95616 USA VA Northern Calif Hlth Care Syst Mather CA 95655 USA Univ Calif Davis Dept Chem Engn Davis CA 95616 USA

G protein-coupled receptors (GPCRs) and G proteins transmit signals from hormones and neurotransmitters across cell membranes, initiating downstream signaling and modulating cellular behavior. Using advanced computer modeling and simulation, we identified atomistic-level structural, dynamic, and energetic mechanisms of norepinephrine (NE) and stimulatory G protein (Gs) interactions with R-adrenergic receptors (RARs), crucial GPCRs for heart function regulation and major drug targets. Our analysis revealed distinct binding behaviors of NE within R1AR and R2AR despite identical orthosteric binding pockets. R2AR had an additional binding site, explaining variations in NE binding affinities. Simulations showed significant differences in NE dissociation pathways and receptor interactions with the Gs. R1AR binds Gs more strongly, while R2AR induces greater conformational changes in the a subunit of Gs. Furthermore, GTP and GDP binding to Gs may disrupt coupling between NE and RAR, as well as between RAR and Gs. These findings may aid in designing precise RAR-targeted drugs.

关键词： Protein structure aspects computational bioinformatics

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bioinformatics Perspective of Drug Repurposing

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CURRENT bioinformatics 2024年第4期19卷 295-315页

作者： Patel, Binita Gelat, Brijesh Soni, Mehul Rathaur, Pooja Johar, S. R. Kaid Gujarat Univ Sch Sci Dept Life Sci Ahmadabad 380009 Gujarat India Gujarat Univ Sch Sci Dept Zool BMTC HG & WBC Ahmadabad 380009 Gujarat India Gujarat Univ Sch Sci Dept Bioinformat Ahmadabad 380009 Gujarat India

Different diseases can be treated with various therapeutic agents. Drug discovery aims to find potential molecules for existing and emerging diseases. However, factors, such as increasing development cost, generic competition due to the patent expiry of several drugs, increase in conservative regulatory policies, and insufficient breakthrough innovations impairs the development of new drugs and the learning productivity of pharmaceutical industries. Drug repurposing is the process of finding new therapeutic applications for already approved, withdrawn from use, abandoned, and experimental drugs. Drug repurposing is another method that may partially overcome the hurdles related to drug discovery and hence appears to be a wise attempt. However, drug repurposing being not a standard regulatory process, leads to administrative concerns and problems. The drug repurposing also requires expensive, high-risk clinical trials to establish the safety and efficacy of the repurposed drug. Recent innovations in the field of bioinformatics can accelerate the new drug repurposing studies by identifying new targets of the existing drugs along with drug candidate screening and refinement. Recent advancements in the field of comprehensive high throughput data in genomics, epigenetics, chromosome architecture, transcriptomic, proteomics, and metabolomics may also contribute to the understanding of molecular mechanisms involved in drug-target interaction. The present review describes the current scenario in the field of drug repurposing along with the application of various bioinformatic tools for the identification of new targets for the existing drug.

关键词： Drug discovery drug repurposing new drug targets computational bioinformatics network pharmacology proteomics

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In silico designing and characterization of outer membrane protein K (OmpK) from Vibrio anguillarum and its expression in Nicotiana tabacum for the development of a plant-based vaccine against fish vibriosis

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JOURNAL OF BIOTECHNOLOGY 2024年 380卷 51-63页

作者： Malik, Muhammad Suleman Elahi, Iqra Sameeullah, Muhammad Ijaz, Fatima Batool, Neelam Khalid, Fatima Gurel, Ekrem Saba, Kiran Waheed, Mohammad Tahir Quaid I Azam Univ Fac Biol Sci Dept Biochem Islamabad Pakistan Bolu Abant Izzet Baysal Univ Fac Agr Dept Field Crops TR-14030 Bolu Turkiye Bolu Abant Izzet Baysal Univ Ctr Innovat Food Technol Dev Applicat & Res TR-14030 Bolu Turkiye Bolu Abant Izzet Baysal Univ Fac Sci & Literature Dept Biol TR-14030 Bolu Turkiye Shaheed Benazir Bhutto Women Univ Fac Life Sci Dept Biochem Peshawar Pakistan

Vibriosis is caused by Vibrio anguillarum in various species of aquaculture. A novel, secure, and stable vaccine is needed to eradicate vibriosis. Here, for reverse vaccinology and plant -based expression, the outer membrane protein K (OmpK) of V. anguillarum was chosen due to its conserved nature in all Vibrio species. OmpK, an ideal vaccine candidate against vibriosis, demonstrated immunogenic, non -allergic, and non-toxic behavior by using various bioinformatics tools. Docking showed the interaction of the OmpK model with TLR-5. In comparison to costly platforms, plants can be used as alternative and economic bio-factories to produce vaccine antigens. We expressed OmpK antigen in Nicotiana tabacum using Agrobacterium-mediated transformation. The expression vector was constructed using Gateway (R) cloning. Transgene integration was verified by polymerase chain reaction (PCR), and the copy number via qRT-PCR, which showed two copies of transgenes. Western blotting detected monomeric form of OmpK protein. The total soluble protein (TSP) fraction of OmpK was equivalent to 0.38% as detected by ELISA. Mice and fish were immunized with plant -derived OmpK antigen, which showed a significantly high level of anti-OmpK antibodies. The present study is the first report of OmpK antigen expression in higher plants for the potential use as vaccine in aquaculture against vibriosis, which could provide protection against multiple Vibrio species due to the conserved nature OmpK antigen.

关键词： Aquaculture Plant-based vaccines OmpK Vibriosis Subunit vaccines Tobacco Transformation computational bioinformatics

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In silico studies reveal the anti-osteosarcoma targets and action mechanisms of resveratrol

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PROCESS BIOCHEMISTRY 2022年第0期117卷 191-197页

作者： Li, Jun Wei, Wenxing Lai, Zongqiang Lai, Keng Po Guangxi Med Univ Pharmaceut Dept Affiliated Hosp 2 Nanning Peoples R China Guangxi Med Univ Clin Med Res Ctr Affiliated Hosp 2 166 Daxuedong Rd Nanning 530007 Guangxi Peoples R China

Previous reports have discovered the anticancer activity of resveratrol in preclinical experiments, especially towards osteosarcoma (OS). However, the comprehensive anti-OS mechanisms of resveratrol remain unexplored. First, we used computational bioinformatics, such as network pharmacology and molecular docking, to identify the putative biotargets and mechanisms of resveratrol against OS. Second, the crucial biotargets identified were validated using cultured samples. All resveratrol and OS-related genes, and overlapping genes were obtained. Enrichment analysis revealed in-depth molecular mechanisms of resveratrol against OS. Other core targets of resveratrol against OS were identified accordingly. The identified core targets of tumor protein p53 (TP53), E1Abinding protein p300 (EP300), sarcoma gene (SRC) were then validated in vitro. We observed that resveratroladministered OS cells exhibited suppressed cell proliferation, increased TP53 protein activation, and decreased cellular EP300 and SRC protein expression levels. Collectively, we report that resveratrol action may regulate multiple targets to exert anti-OS pharmacological responses and may serve as a promising bioactive compound for the clinical treatment of OS.

关键词： Resveratrol Osteosarcoma computational bioinformatics Biotargets Biochemical validation

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Multiobjective Optimization Applied to the Eradication of Persistent Pathogens

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2010年第5期14卷 759-765页

作者： Steuernagel, Ole Polani, Daniel Univ Hertfordshire Sch Phys Astron & Math Hatfield AL10 9AB Herts England Univ Hertfordshire Sch Comp Sci Hatfield AL10 9AB Herts England

In scenarios such as therapeutic modeling or pest control, one aims to suppress infective agents or maximize crop yields while minimizing the side-effects of interventions, such as cost, environmental impact, and toxicity. Here, we consider the eradication of persistent microbes (e. g., Escherichia coli, multiply resistant Staphylococcus aureus (MRSA-"superbug"), Mycobacterium tuberculosis, Pseudomonas aeruginosa) through medication. Such microbe populations consist of metabolically active and metabolically inactive (persistent) subpopulations. It turns out that, for efficient medication strategies, the two goals, eradication of active bacteria on one hand and eradication of inactive bacteria on the other, are in conflict. Using multiobjective optimization, we obtain a survey of the full spectrum of best solutions. We find that, if treatment time is limited and the total medication dose is constant, the application of the medication should be concentrated both at the beginning and end of the treatment. If the treatment time is increased, the medication should become increasingly spread out over the treatment period until it is uniformly spread over the entire period. The transition between short and long overall treatment times sees optimal medication strategies clustered into groups.

关键词： Biochemistry biological systems biology biomedical chemistry computational bioinformatics computational intelligence ecology evolutionary biology evolvable hardware game theory mathematics

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Improving genome-scale metabolic models of incomplete genomes with deep learning

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ISCIENCE 2024年第12期27卷 111349页

作者： Boer, Meine D. Melkonian, Chrats Zafeiropoulos, Haris Haas, Andreas F. Garza, Daniel R. Dutilh, Bas E. Univ Utrecht Theoret Biol & Bioinformat NL-3584 CH Utrecht Netherlands NIOZ Royal Netherlands Inst Sea Res Dept Marine Microbiol & Biogeochem POB 59 NL-1790 AB Den Burg Netherlands Wageningen Univ & Res Bioinformat Grp Wageningen Netherlands Katholieke Univ Leuven Rega Inst Med Res Dept Microbiol Immunol & Transplantat Lab Mol Bacteriol B-3000 Leuven Belgium Univ Paris Saclay INRAE PROSE F-92761 Antony France Friedrich Schiller Univ Jena Inst Biodivers Fac Biol Sci Cluster Excellence Balance Microverse D-07743 Jena Germany

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.

关键词： Microbial genomics Biocomputational method computational bioinformatics Genomic analysis

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Histopathological Image QTL Discovery of Immune Infiltration Variants

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ISCIENCE 2018年第July 27, 2018期5卷 80-+页

作者： Barry, Joseph D. Fagny, Maud Paulson, Joseph N. Aerts, Hugo J. W. L. Platig, John Quackenbush, John Dana Farber Canc Inst Ctr Canc Computat Biol 450 Brookline Ave Boston MA 02215 USA Dana Farber Canc Inst Dept Biostat & Computat Biol 450 Brookline Ave Boston MA 02215 USA Harvard TH Chan Sch Publ Hlth Dept Biostat 667 Huntington Ave Boston MA 02115 USA Dana Farber Canc Inst Dept Radiol 450 Brookline Ave Boston MA 02215 USA Alnylam Pharmaceut 300 Third St 3 Cambridge MA 02142 USA

Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image quantitative trait loci (iQTLs) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype-Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.

关键词： Association Analysis bioinformatics computational bioinformatics Pathology

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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

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ISCIENCE 2018年第November 30, 2018期9卷 451-+页

作者： Wang, Zeya Cao, Shaolong Morris, Jeffrey S. Ahn, Jaeil Liu, Rongjie Tyekucheva, Svitlana Gao, Fan Li, Bo Lu, Wei Tang, Ximing Wistuba, Ignacio I. Bowden, Michaela Mucci, Lorelei Loda, Massimo Parmigiani, Giovanni Holmes, Chris C. Wang, Wenyi Univ Texas MD Anderson Canc Ctr Dept Bioinformat & Computat Biol Houston TX 77030 USA Rice Univ Dept Stat Houston TX 77005 USA Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77030 USA Georgetown Univ Dept Biostat & Bioinformat Washington DC 20057 USA Dana Farber Canc Inst Dept Biostat & Computat Biol Boston MA 02215 USA Harvard Univ Dept Stat Cambridge MA 02138 USA Univ Texas MD Anderson Canc Ctr Dept Translat Mol Pathol Houston TX 77030 USA Dana Farber Canc Inst Dept Oncol Pathol Boston MA 02215 USA Harvard TH Chan Sch Publ Hlth Dept Epidemiol Boston MA 02115 USA Brigham & Womens Hosp Dept Pathol Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA 02115 USA Univ Oxford Dept Stat Oxford OX1 3LB England

Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: hftps://***/wwylab/DeMixTallmaterials.

关键词： Cancer computational bioinformatics Transcriptomics

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Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network

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ISCIENCE 2020年第7期23卷 101261页

作者： Yi, Hai-Cheng You, Zhu-Hong Huang, De-Shuang Guo, Zhen-Hao Chan, Keith C. C. Li, Yangming Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Xinjiang Lab Minor Speech & Language Informat Pro Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Tongji Univ Sch Elect & Informat Engn Inst Machine Learning & Syst Biol Shanghai 201804 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China Rochester Inst Technol Coll Engn Technol Rochester NY 14623 USA

Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a randomforest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components.

关键词： bioinformatics Biocomputational Method computational bioinformatics

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