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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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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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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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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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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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ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

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ISCIENCE 2024年第3期27卷 109198页

作者： Teng, Pang-ning Schaaf, Joshua P. Abulez, Tamara Hood, Brian L. Wilson, Katlin N. Litzi, Tracy J. Mitchell, David Conrads, Kelly A. Hunt, Allison L. Olowu, Victoria Oliver, Julie Park, Fred S. Edwards, Marshe Chiang, Aichun Wilkerson, Matthew D. Raj-Kumar, Praveen-Kumar Tarney, Christopher M. Darcy, Kathleen M. Phippen, Neil T. Maxwell, G. Larry Conrads, Thomas P. Bateman, Nicholas W. Gynecol Canc Ctr Excellence Annandale VA 22003 USA Womens Hlth Integrated Res Ctr Annandale VA 22003 USA Henry M Jackson Fdn Adv Mil Med Inc Bethesda MD 20817 USA Inova Hlth Syst Womens Hlth Integrated Res Ctr Womens Serv Line Falls Church VA 22042 USA Uniformed Serv Univ Hlth Sci Ctr Mil Precis Hlth Dept Anat Physiol & Genet Bethesda MD 20814 USA Chan Soon Shiong Inst Mol Med Windber PA 15963 USA Uniformed Serv Univ Hlth Sci John P Murtha Canc Ctr Dept Surg Bethesda MD 20889 USA Walter Reed Natl Mil Med Ctr Bethesda MD 20889 USA

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript -level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co -quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r R 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein -based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://***/ProteoMixture/.

关键词： computational bioinformatics Proteomics Transcriptomics

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Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models

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ISCIENCE 2024年第7期27卷 110358页

作者： Palanikumar, Indumathi Sinha, Himanshu Raman, Karthik Indian Inst Technol IIT Madras Bhupat Jyoti Mehta Sch Biosci Dept Biotechnol Chennai 600036 India IIT Madras Ctr Integrat Biol & Syst Med IBSE Chennai 600036 India IIT Madras Robert Bosch Ctr Data Sci & Artificial Intelligen Chennai 600036 India IIT Madras Wadhwani Sch Data Sci & AI Dept Data Sci & AI Chennai 600036 Tamil Nadu India

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce " Panera ,"an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, " Panera "presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

关键词： Microbiology bioinformatics computational bioinformatics Systems biology

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Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization

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ISCIENCE 2024年第6期27卷 109961页

作者： Fan, Qinghua Wen, Shifeng Zhang, Yi Feng, Xiuming Zheng, Wanting Liang, Xiaolin Lin, Yutong Zhao, Shimei Xie, Kaisheng Jiang, Hancheng Tang, Haifeng Zeng, Xiangtai Guo, You Wang, Fei Yang, Xiaobo Guangxi Med Univ Sch Publ Hlth Nanning 530000 Guangxi Peoples R China Guangxi Univ Sci & Technol Affiliated Hosp 2 Guangxi Key Lab Precise Prevent & Treatment Thyroi Liuzhou 545000 Guangxi Peoples R China Zhengzhou Univ Affiliated Hosp 1 Zhengzhou 450000 Henan Peoples R China Guangxi Univ Sci & Technol Affiliated Hosp 2 Liuzhou 545000 Guangxi Peoples R China Liuzhou Workers Hosp Liuzhou 545000 Guangxi Peoples R China Second Peoples Hosp Yulin Yulin 537000 Guangxi Peoples R China Gannan Med Univ Affiliated Hosp 1 Ganzhou 341000 Jiangxi Peoples R China

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (n Case = 3,418, n Control = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections ( P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

关键词： computational bioinformatics cancer

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Safeguarding authenticity for mitigating the harms of generative AI: Issues, research agenda, and policies for detection, fact-checking, and ethical AI

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ISCIENCE 2024年第2期27卷 108782页

作者： Hamed, Ahmed Abdeen Zachara-Szymanska, Malgorzata Wu, Xindong Sano Ctr Computat Med Network Med & AI Czarnowiejska 36 Bldg C5 PL-30072 Krakow Poland Jagiellonian Univ Fac Int & Polit Studies Wladys Lawa Reymonta 4 PL-30059 Krakow Poland Zhejiang Lab Res Ctr Knowledge Engn Kechuang Ave Hangzhou 311121 Zhejiang Peoples R China

As the influence of transformer-based approaches in general and generative artificial intelligence (AI) in particular continues to expand across various domains, concerns regarding authenticity and explainability are on the rise. Here, we share our perspective on the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science. We recognize the transformative potential of generative AI, exemplified by ChatGPT, in the scientific landscape. However, we also emphasize the urgency of addressing associated challenges, particularly in light of the risks posed by disinformation, misinformation, and unreproducible science. This perspective serves as a response to the call for concerted efforts to safeguard the authenticity of information in the age of AI. By prioritizing detection, fact-checking, and explainability policies, we aim to foster a climate of trust, uphold ethical standards, and harness the full potential of AI for the betterment of science and society.

关键词： Biocomputational method bioinformatics Biological sciences computational bioinformatics Natural sciences Neural networks Artificial intelligence Artificial intelligence applications

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