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Dormancy heterogeneity among Arabidopsis thaliana seeds is linked to individual seed size

Plant Communications 2024年第2期5卷 215-227页

作者： Michal Krzyszton Sebastian P.Sacharowski Veena Halale Manjunath Katarzyna Muter Grzegorz Bokota Ce Wang Dariusz Plewczynski Tereza Dobisova Szymon Swiezewski Laboratory of Seeds Molecular Biology Institute of Biochemistry and BiophysicsPAS02-106 WarsawPoland Laboratory of Functional and Structural Genomics Centre of New TechnologiesUniversity of WarsawWarsawPoland Laboratory of Bioinformatics and Computational Genomics Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland Labdeers Ltd. 680-01 BoskoviceCzech Republic

Production of morphologically and physiologically variable seeds is an important strategy that helps plants to survive in unpredictable natural ***,the model plant Arabidopsis thaliana and most agronomically essential crops produce visually homogenous *** automated phenotype analysis,we observed that small seeds in Arabidopsis tend to have higher primary and secondary dormancy levels than large *** analysis revealed distinct gene expression profiles between large and small *** seeds have higher expression of translation-related genes implicated in germination *** contrast,small seeds have elevated expression of many positive regulators of dormancy,including a key regulator of this process,the DOG1 *** in DOG1 expression are associated with differential production of its alternative cleavage and polyadenylation isoforms;in small seeds,the proximal poly(A)site is selected,resulting in a short mRNA ***,single-seed RNA sequencing analysis demonstrated that large seeds resemble DOG1 knockout mutant ***,on the single-seed level,expression of genes affected by seed size is correlated with expression of genes that position seeds on the path toward *** results demonstrate an unexpected link between seed size and dormancy phenotypes in a species that produces highly homogenous seed pools,suggesting that the correlation between seed morphology and physiology is more widespread than initially assumed.

关键词： seed size dormancy DOG1 Arabidopsis

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Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

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细胞研究（英文版） 2021年第5期31卷 495-516页

作者： Lei Sun Kui Xu Wenze Huang Yucheng T.Yang Pan Li Lei Tang Tuanlin Xiong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06520USA Department of Molecular Biophysics and Biochemistry Yale UniversityNew HavenCT 06520USA

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation,and dynamically reflect specific cellular ***,presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures,and therefore do not capture their condition-dependent ***,after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types,we developed PrismNet,a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular *** results for 168 RBPs support its utility for both understanding CUP-seq results and largely extending such interaction data to accurately analyze additional cell ***,PrismNet employs an 'attention' strategy to computationally identify exact RBP-binding nucleotides,and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants(riboSNitches),which can link genetic diseases with dysregulated RBP *** rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions,with clear utility for understanding and treating human diseases.

关键词： bioinformatics Mechanisms of disease Molecular biology

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Molecular Basis for pH Sensing in the KDEL Trafficking Receptor

SSRN

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SSRN 2023年

作者： Wu, Zhiyi Smith, Kathryn Gerondopoulos, Andreas Sobajima, Tomoaki Parker, Joanne L. Barr, Francis A. Newstead, Simon Biggin, Philip C. Peek, Sneak Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford OxfordOX1 3QU United Kingdom Department of Biochemistry University of Oxford OxfordOX1 3QU United Kingdom Kavli Institute for Nanoscience Discovery University of Oxford OxfordOX1 3QU United Kingdom

Selective transport of proteins between organelles is crucial to establish and maintain different chemical environments within eukaryotic cells. Trafficking receptors control protein localisation through the recognition of signal sequences specifying different locations in the cell. Differences in the luminal pH or organelles are important for their functions, for the vectorial trafficking of cargo receptors. The KDEL receptor, a heptahelical membrane protein, is responsible for maintaining the integrity of the ER by retrieving soluble folding chaperones in a pH dependent mechanism. Recent structural and biochemical studies have revealed the end states of KDEL receptor activation and mechanism of selective cargo binding. However, the crucial mechanism by which the KDEL receptor responds to changes in luminal pH remains unclear. To explain the mechanism by which protonation acts in this system, we combine analysis of X-ray crystal structures of the KDEL receptor at neutral and acidic pH with advanced computational methods and cell-based assays. This integrated approach reveals a critical role for order water molecules and allows us to infer a direct connection between protonation in different cellular compartments and the consequent changes in the affinity of the receptor for cargo. © 2023, The Authors. All rights reserved.

关键词： Protonation

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Subdomain dynamics enable chemical chain reactions in non-ribosomal peptide synthetases

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Nature Chemistry 2024年第2期16卷 259-268页

作者： Sun, Xun Alfermann, Jonas Li, Hao Watkins, Maxwell B. Chen, Yi-Tsao Morrell, Thomas E. Mayerthaler, Florian Wang, Chia-Ying Komatsuzaki, Tamiki Chu, Jhih-Wei Ando, Nozomi Mootz, Henning D. Yang, Haw Department of Chemistry Princeton University Princeton NJ United States Institute of Biochemistry University of Münster Münster Germany Department of Chemistry and Chemical Biology Cornell University Ithaca NY United States Institute of Bioinformatics and Systems Biology Institute of Molecular Medicine and Bioengineering Department of Biological Science and Technology Centre for Intelligent Drug Systems and Smart Bio-devices (IDS²B) National Yang Ming Chiao Tung University Hsinchu Taiwan Research Centre of Mathematics for Social Creativity Research Institute for Electronic Science The Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University Sapporo Japan The Institute of Scientific and Industrial Research Osaka University Ibaraki Japan Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla CA United States

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states;yet how these molecular machines orchestrate such biochemical sequences has remained elusive. Here, using single-molecule Förster resonance energy transfer, we show that the A-domain of gramicidin S synthetase I adopts structurally extended and functionally obligatory conformations for alternating between adenylation and thioester-formation structures during enzymatic cycles. Complementary biochemical, computational and small-angle X-ray scattering studies reveal interconversion among these three conformations as intrinsic and hierarchical where intra-A-domain organizations propagate to remodel inter-A–PCP didomain configurations during catalysis. The tight kinetic coupling between structural transitions and enzymatic transformations is quantified, and how the gramicidin S synthetase I A-domain utilizes its inherent conformational dynamics to drive directional biosynthesis with a flexibly linked PCP domain is revealed. [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Enzyme mechanisms Reaction kinetics and dynamics SAXS Single-molecule biophysics

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Virtual Flavonoids Screening Identifies Potent Compounds Against Enterovirus A71 and Coxsackievirus A16

SSRN

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SSRN 2023年

作者： Sripattarphan, Amita Sanachai, Kamonpan Chavasiri, Warinthorn Wolschann, Peter Langer, Thierry Boonyasuppayakorn, Siwaporn Rungrotmongkol, Thanyada Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Khon Kaen University Khon Kaen40002 Thailand Center of Excellence in Natural Products Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Pharmaceutical Chemistry Faculty of Life Sciences University of Vienna Althanstraße 14 ViennaA-1090 Austria Institute of Theoretical Chemistry University of Vienna Vienna1090 Austria Center of Excellence in Applied Medical Virology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

Worldwide outbreaks of hand, foot, and mouth disease (HFMD) are caused by enterovirus A71 (EV-A71) and coxsackievirus A16 (CV-A16). Since no anti-HFMD drugs are currently available, it is interesting to study potential viral inhibitors. Rupintrivir is a rhinovirus 3C protease (3Cpro) inhibitor with apparent inhibitory activity against HFMD. This study constructs pharmacophore models of the EV-A71 and CV-A16 3Cpro complexed with rupintrivir using molecular dynamics (MD) simulations. Both models had similar pharmacophore features, including hydrogen bond donors and acceptors and hydrophobic interactions. These pharmacophore models were then used as a template to screen 39 flavonoid compounds as potential novel inhibitors. Diosmin, epigallocatechin gallate (EGCG), and RTH-011 showed high binding affinities for EV-A71 and CV-A16 3Cpro. They formed hydrogen bonds with important surrounding residues in both proteins, including H40, L127, T142, A144, T145, H161, I162, G163, and G164. In addition, their effective concentrations against rhabdomyosarcoma (RD) cell infection by EV-A71 and CV-A16 were determined. EGCG had the highest half maximal effective concentration (EC50) of 12.86 ± 1.30 µM for EV-A71 and 15.54 ± 1.50 µM for CV-A16, while diosmin had EC50 of 21.01±1.57 µM for EV-A71 and 30.68 ± 3.25 µM for CV-A16. Both compounds were non-toxic in RD cells, with 50% cytotoxic concentrations of >100 µM for EGCG and >500 µM for diosmin. In addition, MD simulation analysis showed that EGCG had a higher binding affinity than diosmin, supported by its significantly lower solvated binding free energies and greater numbers of contact atoms and key binding residues. Moreover, previous studies reported EGCG’s inhibitory effect on other viruses, such as severe acute respiratory syndrome coronavirus 2. Therefore, our findings suggest that EGCG can effectively inhibit the EV-A71 and CV-A16 3Cpro. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource (vol 52, pg 999, 2020)

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NATURE GENETICS 2021年第2期53卷 254-254页

作者： Portelli, Stephanie Olshansky, Moshe Rodrigues, Carlos H. M. D'Souza, Elston N. Myung, Yoochan Silk, Michael Alavi, Azadeh Pires, Douglas E. V. Ascher, David B. Structural Biology and Bioinformatics Department of Biochemistry Bio21 Institute University of Melbourne Melbourne Victoria Australia Computational Biology and Clinical Informatics Baker Heart and Diabetes Institute Melbourne Victoria Australia School of Computing and Information Systems University of Melbourne Melbourne Victoria Australia Department of Biochemistry University of Cambridge Cambridge UK

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Buparlisib and ponatinib inhibit aggressiveness of cholangiocarcinoma cells via suppression of IRS1-related pathway by targeting oxidative stress resistance

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Biomedicine and Pharmacotherapy 2024年 180卷 117569-117569页

作者： Kaewlert, Waleeporn Sakonsinsiri, Chadamas Lert-itthiporn, Worachart Mahalapbutr, Panupong Ali, Saba Rungrotmongkol, Thanyada Jusakul, Apinya Armartmuntree, Napat Pairojkul, Chawalit Feng, Guofei Ma, Ning Pinlaor, Somchai Murata, Mariko Thanan, Raynoo Department of Biochemistry Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Cholangiocarcinoma Research Institute Khon Kaen University Khon Kaen 40002 Thailand Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Program in Bioinformatics and Computational Biology Graduated School Chulalongkorn University Bangkok 10330 Thailand The Center for Research and Development of Medical Diagnostic Laboratories Faculty of Associated Medical Sciences Khon Kaen University Khon Kaen 40002 Thailand Department of Medical Science Amnatcharoen Campus Mahidol University Amnat Charoen 37000 Thailand Department of Pathology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Department of Environmental and Molecular Medicine Mie University Graduate School of Medicine Mie 514-8507 Japan Graduate School of Health Science Suzuka University of Medical Science Suzuka Mie 510-0226 Japan Department of Parasitology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand

Cholangiocarcinoma (CCA) is an oxidative stress-driven liver cancer with bile duct epithelial cell phenotypes and currently lacks effective treatments, making targeted drug therapy urgently needed. Oxidative stress plays a critical role in CCA carcinogenesis, involving cells with oxidative stress resistance via upregulation of the PI3K and MEKK3 signaling pathways. In this study, we investigated the antineoplastic efficacy of a PI3K inhibitor (buparlisib) and a multi-tyrosine kinase inhibitor (ponatinib) on CCA. The cytotoxicity of the drug combination was studied in vitro using CCA cell lines and in vivo using CCA xenograft models. It was found that the drug combination suppressed growth, colony formation, and migration abilities of CCA cells and induced oxidative damage, cell cycle arrest, and autophagy by suppressing MEKK3 and YAP1 through inhibition of insulin receptor substrate 1 (IRS1) signaling. Moreover, the drugs would potentially bind to the IRS1 protein, significanly decreasing IRS1 phosphorylation. Additionally, the drug combination significantly diminished the expression of YAP1, the cell proliferation marker and an antioxidant regulator, and increased oxidative stress-responsive markers in the xenograft model. In conclusion, targeting oxidative stress resistance with combined buparlisib and ponatinib suppressed tumor growth and migration by repressing IRS1-related pathways and ultimately inducing oxidative damage, suggesting the potential for targeted therapy and clinical trials in CCA patients over the use of a single drug. © 2024 The Authors

关键词： Anti-cancer Bile duct cancer Buparlisib Insulin receptor substrate 1 Ponatinib Targeted therapy

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The need to implement FAIR principles in biomolecular simulations

arXiv

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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Correction: Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts

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Physical chemistry chemical physics : PCCP 2023年第29期25卷 20145页

作者： David Seiferth Stephen J Tucker Philip C Biggin Clarendon Laboratory Department of Physics University of Oxford Oxford OX1 3PU UK. Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford OX1 3QU UK. philip.biggin@bioch.ox.ac.uk. Kavli Institute for Nanoscience Discovery University of Oxford Oxford UK.

Correction for 'Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts' by David Seiferth , , 2023, , 17596-17608, https://***/10.1039/D3CP00479A.

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An in-silico approach for structural and functional annotation of an uncharacterized protein of human papillomavirus (HPV)

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Current Proteomics 2025年第1期22卷

作者： Md. Hasan Ali Dipa Bhattacharjee Titly Biswas Ritu Md. Zaber Sadman Suraiya Zaman Abu Saim Mohammad Saikat Department of Computational Biology and Bioinformatics Advanced Bioscience Center for Collaborative Research (ABSCCR) Rajshahi 6250 Bangladesh Department of Agriculture Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh Department of Biochemistry and Molecular Biology Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh Structural & Chemical Biology Department Centro de Investigaciones Biologicas Margarita Salas CIB_CSIC Madrid 28040 Spain

Human papillomavirus (HPV) is the predominant viral infection, which causes various new cancer cases globally each year, encompassing cervical and various other cancer types. HPVs have developed throughout the course of millions of years to replicate inside various animal species, including humans. Co-evolved viruses, in this manner, gradually induce persistent asymptomatic infections, characterized by virion production without evident illness. This study aims to assess the HPV protein sequence and perform structure-based functional evaluations, collecting data on its physicochemical properties, domain predictions for targeted functional forecasts, and protein-protein interaction networks, as well as predicting and analyzing both 2D and 3D structures. The physicochemical analysis revealed that the protein contained a greater number of negatively charged residues compared to positively charged residues in its sequence. The GRAVY value indicated that this protein exhibits hydrophilic characteristics. The protein contains a polyadenylate-binding protein that binds to the poly(A) tail of the mRNA and acts as a post-transcriptional regulator. Gene ontology analysis has explained protein associations within cellular, molecular, and biological processes. An interaction between the specified protein and ten other proteins was established by the PPI network. The evaluation of the secondary structure indicated that the random coil was the predominant structural element, followed by the alpha helix and extended strand. Furthermore, the protein's 3D structure was predicted using three distinct programs: AlphaFold, I-TASSER, and SWISS-MODEL. Following the evaluation of the 3D structure, the result showed that SWISS-MODEL predicted a superior configuration. The functional protein is associated with post-transcriptional regulation and can be targeted to model novel therapeutic interventions against the infection caused by HPV.

关键词： Human papillomavirus HPV Polyadenylate-binding protein Post-transcriptional regulator PPI network

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