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Artigos de revistas sobre o assunto "AlphaFold"
Finkelstein, Alexei V. "Protein 3D Structure Identification by AlphaFold: a Physics-Based Prediction or Recognition Using Huge Databases?" Journal of Molecular Biology 6, n.º 1 (20 de março de 2024): 1–10. http://dx.doi.org/10.52338/tjomb.2024.3935.
Texto completo da fonteWheeler, Richard John. "A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure". PLOS ONE 16, n.º 11 (11 de novembro de 2021): e0259871. http://dx.doi.org/10.1371/journal.pone.0259871.
Texto completo da fonteWen, Haosheng, Wei-Hsiang Weng e Marcos Sotomayor. "A curated AlphaFold 2/AlphaFill cadherinosome". Biophysical Journal 122, n.º 3 (fevereiro de 2023): 474a—475a. http://dx.doi.org/10.1016/j.bpj.2022.11.2544.
Texto completo da fonteManabe, Noriyoshi. "AlphaFill: Docking Ligands and Cofactors into AlphaFold Models". Trends in Glycoscience and Glycotechnology 35, n.º 206 (25 de julho de 2023): E61. http://dx.doi.org/10.4052/tigg.2316.6e.
Texto completo da fonteAlQuraishi, Mohammed. "AlphaFold at CASP13". Bioinformatics 35, n.º 22 (22 de maio de 2019): 4862–65. http://dx.doi.org/10.1093/bioinformatics/btz422.
Texto completo da fonteVaradi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan et al. "AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models". Nucleic Acids Research 50, n.º D1 (17 de novembro de 2021): D439—D444. http://dx.doi.org/10.1093/nar/gkab1061.
Texto completo da fontePak, Marina A., Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov, Ilya S. Vorobyev, Ekaterina S. Maksimova, Fyodor A. Kondrashov e Dmitry N. Ivankov. "Using AlphaFold to predict the impact of single mutations on protein stability and function". PLOS ONE 18, n.º 3 (16 de março de 2023): e0282689. http://dx.doi.org/10.1371/journal.pone.0282689.
Texto completo da fonteLe Page, Michael. "Why AlphaFold is transformational". New Scientist 255, n.º 3398 (agosto de 2022): 11. http://dx.doi.org/10.1016/s0262-4079(22)01373-2.
Texto completo da fonteLaura Howes. "AlphaFold goes all atom". C&EN Global Enterprise 101, n.º 37 (13 de novembro de 2023): 6. http://dx.doi.org/10.1021/cen-10137-scicon3.
Texto completo da fontePorta-Pardo, Eduard, Victoria Ruiz-Serra, Samuel Valentini e Alfonso Valencia. "The structural coverage of the human proteome before and after AlphaFold". PLOS Computational Biology 18, n.º 1 (24 de janeiro de 2022): e1009818. http://dx.doi.org/10.1371/journal.pcbi.1009818.
Texto completo da fonteTeses / dissertações sobre o assunto "AlphaFold"
Gosset, Simon. "Annotation du proteome d'A. thaliana via l'analyse et la prédiction de son interactome". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL004.
Texto completo da fonteLiving organisms function thanks to a set of interactions between molecules called proteins. Identifying the pairs of proteins involved in these interactions provides a better understanding of how they function. There are several experimental methods for identifying these proteins, but they are either too expensive or not reliable enough to explore all the protein interactions that take place in living organisms. Computational methods have been developed to predict these interactions to meet this need. During my thesis, I set up a method for predicting protein-protein interactions using Alphafold-multimer, an innovative structure prediction method. At the same time, I produced a dataset describing the physico-chemical characteristics, binding energy and interaction propensity of the surfaces and interfaces of a large number of proteins to understand what distinguishes correctly predicted PPIs from incorrectly predicted PPI by the method I have set up
Aimeur, Sana. "Exploration de l'interaction p22phox/p47phox : une clé pour comprendre l'assemblage du complexe NADPH oxydase". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASF099.
Texto completo da fonteThe phagocytic NADPH oxidase complex (NOX2) plays a pivotal role in generating reactive oxygen species, crucial for innate immunity and various physiological processes. Its dysregulation contributes to oxidative stress, aging, and inflammation. This complex enzyme comprises six subunits, including two membrane subunits, NOX2 and p22phox, and four cytosolic proteins (p47phox, p67phox, p40phox, and Rac). NOX2 and p22phox, encoded by the CYBB and CYBA genes, respectively, form the catalytic core of the phagocyte NADPH oxidase complex. This core, also known as flavocytochrome b558 (Cytb558), contains all redox intermediates for electron transfers needed to reduce oxygen to superoxide anions. Despite ongoing challenges like the complex's flexibility, understanding the structure and function of these subunits is an expanding research area. The enzyme's activation first requires the interaction between the p47phox protein and the p22phox membrane subunit. Investigating these proteins, particularly their interaction, is challenging due to their intrinsic properties, such as disordered regions, membrane domains, and highly flexible domain organization. Although several studies have been conducted, the molecular mechanisms involved remain to be clarified. This thesis focuses on several aspects of this interaction, addressing the issue by combining biochemical techniques (for both soluble and membrane proteins) with circular dichroism studies, nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and structure predictions by artificial intelligence (AlphaFold). These complementary approaches have enabled the proposal of a scenario demonstrating the conformational changes of p47phox from its inactive state to active states. These structural changes of p47phox have also been explored in the context of its interaction with p22phox, revealing previously unknown structural aspects of this interaction. Concurrently, artificial intelligence through AlphaFold has provided the first structural models of the assembled enzyme complex, allowing exploration of protein-protein and protein-lipid membrane interactions, which should encourage more in-depth future studies. Finally, the development of production and purification methods for recombinant membrane protein p22phox, particularly in native nanodiscs, adds new perspectives to this work that blends experimental and theoretical approaches
Launay, Romain. "Computational characterization and understanding of protein assemblies : the case of the Escherichia coli Ubi metabolon involved in ubiquinone biosynthesis". Electronic Thesis or Diss., Toulouse, INSA, 2023. http://www.theses.fr/2023ISAT0055.
Texto completo da fonteProtein-protein interactions (PPIs) and supramolecular assemblies are essential for the functions of living cells. They play an important role in various biological functions, such as signal transduction, cell-cell communication, transcription, replication and membrane transport. Determining and characterizing such interfaces remains a challenge in structural biology. However, advances in the development of computational methods and the power of the computing resources available today have led to a considerable improvement in the accuracy of in silico predictions of three-dimensional models of protein assemblies.In this thesis, the aim was to predict the structure of a supramolecular assembly, called the Ubi metabolon, involved in the ubiquinone (UQ8) biosynthesis pathway in Escherichia coli. Ubiquinone is a prenol with oxido-reducing properties, localized in membranes, and highly conserved throughout evolution but also in different cells of organisms. It is composed of two main parts, an aromatic group with oxido-reducing properties, known as quinone or polar head, and a polyisoprenoid tail which is hydrophobic in nature. In this study, we are interested in the final stages of the biosynthetic pathway, in particular the modifications (methylations and hydroxylations) of the polar head. These reactions take place within the Ubi metabolon. The latter is made up of seven different proteins (UbiE, UbiG, UbiF, UbiH, UbiI, UbiJ, UbiK) catalysing six consecutive enzymatic reactions.In this work, we sought to predict the structure of the metabolon and were thus able to propose a protein subset that we called the 'core subunit'. This sub-unit includes all the partners and could be biologically functional. In parallel, a study was carried out on the UbiJ-UbiK2 heterotrimer, an essential molecular brick of the Ubi metabolon. A three-dimensional model of UbiJ-UbiK2 was proposed. Using a multi-scale modelling study, it was shown that it could be involved in the release of ubiquinone from membranes. Finally, the last part of this work focused on studying the behavior of a particular family of enzymes, the class A flavin mono-oxygenases, to which UbiF, UbiH and UbiI belong. A comparative study between a representative enzyme from this family, called PHBH, and UbiI was carried out, concluding that interactions with partners were necessary to stabilize these proteins within the Ubi metabolon.Taken together, this work and the proposed hypotheses provide a new insight into the supramolecular organization of the Ubi metabolon, both structurally and functionally. Our results open up new prospects for their experimental study
Bruley, Apolline. "Exploitation de signatures des repliements protéiques pour décrire le continuum ordre/désordre au sein des protéomes". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS474.
Texto completo da fonteA significant fraction of the proteomes remains unannotated, leaving inaccessible a part of the functional repertoire of life, including molecular innovations with therapeutic or environmental value. Lack of functional annotation is partly due to the limitations of the current approaches in detecting hidden relationships, or to specific features such as disorder. The aim of my PhD thesis was to develop methodological approaches based on the structural signatures of folded domains, in order to further characterize the protein sequences with unknown function even in absence of evolutionary information. First, I developed a scoring system in order to estimate the foldability potential of an amino acid sequence, based on its density in hydrophobic clusters, which mainly correspond to regular secondary structures. I disentangled the continuum between order and disorder, covering various states from extended conformations (random coils) to molten globules and characterize cases of conditional order. Next, I combined this scoring system with the AlphaFold2 (AF2) 3D structure predictions available for 21 reference proteomes. A large fraction of the amino acids with very low AF2 model confidence are included in non-foldable segments, supporting the quality of AF2 as a predictor of disorder. However, within each proteome, long segments with very low AF2 model confidence also exhibit characteristics of soluble, folded domains. This suggests hidden order (conditional or unconditional), which is undetected by AF2 due to lack of evolutionary information, or unrecorded folding patterns. Finally, using these tools, I made a preliminary exploration of unannotated proteins or regions, identified through the development and application of a new annotation workflow. Even though these sequences are enriched in disorder, an important part of them showcases soluble globular-like characteristics. These would make good candidates for further experimental validation and characterization. Moreover, the analysis of experimentally validated de novo genes allowed me to contribute to the still-open debate on the structural features of proteins encoded by these genes, enriched in disorder and displaying a great diversity of structura
Capítulos de livros sobre o assunto "AlphaFold"
Laurent, Nussaume, Desnos Thierry, Jinsheng Zhu, David Pascale, Kumiko Miwa e Kanno Satomi. "Analysis and Comparison of Alphafold-Structure Predictions between Pi-Uptake Transporters Recovering Phosphate in Natural Environments". In Plant Phosphorus Nutrition, 129–50. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003440079-9.
Texto completo da fonteMoriarty, David. "Alphafold 2". In Designing and Managing Complex Systems, 263–66. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-91609-7.00026-3.
Texto completo da fonteMariano, Diego. "AlphaFold e a busca pelo Santo Graal da Biologia Molecular". In BIOINFO #02 - Revista Brasileira de Bioinformática e Biologia Computacional, 162–67. 2a ed. Alfahelix, 2022. http://dx.doi.org/10.51780/978-65-992753-5-7-10.
Texto completo da fonteJones, David T., e Janet M. Thornton. "AlphaFold — The End of the Protein Folding Problem or the Start of Something Bigger?" In Artificial Intelligence for Science, 67–80. WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811265679_0005.
Texto completo da fonteBlaney, Jeff, e Andrew M. Davis. "Structure-based Design for Medicinal Chemists". In The Handbook of Medicinal Chemistry, 137–87. The Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781788018982-00137.
Texto completo da fonteGanguly, Rik, Shashi Kumar Yadav, Prosperwell Ingty, Angneh Ngoruh e Atanu Bhattacharjee. "LEVERAGING THE STRENGTH OF ARTIFICIAL INTELLIGENCE IN SOLVING PROTEIN STRUCTURES BY ALPHAFOLD-2- A MODERN APPROACH TO UNDERSTAND PROTEIN DYNAMICS". In Futuristic Trends in Biotechnology Volume 2 Book 27, 234–45. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs27p3ch1.
Texto completo da fonteMuhammed, Muhammed Tilahun, e Esin Aki-Yalcin. "Up-to-Date Developments in Homology Modeling". In Applied Computer-Aided Drug Design: Models and Methods, 116–35. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815179934123010006.
Texto completo da fonteMubarak, Malad, Aastha Senapati, Purva Bankar, Nirmitee Dolas, Jyoti Srivastava, Shankar Mukundrao Khade e Krishna Kant Pandey. "Introduction to AI in Biotechnology and Biomedical Engineering". In Future of AI in Biomedicine and Biotechnology, 1–17. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3629-8.ch001.
Texto completo da fonteTrabalhos de conferências sobre o assunto "AlphaFold"
Maghiar, Octavian-Florin. "AlphaFold-based protein analysis pipeline". In 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2022. http://dx.doi.org/10.1109/synasc57785.2022.00061.
Texto completo da fonteСмирнов, Сергей Владимирович. "SOLVING CURRENT PROBLEMS USING NEURAL NETWORKS". In Современные методы и инновации в науке: сборник статей XXIII международной научной конференции (Санкт-Петербург, Декабрь 2023). Crossref, 2024. http://dx.doi.org/10.37539/231208.2023.38.36.003.
Texto completo da fonteTan, Juntao, e Yongfeng Zhang. "ExplainableFold: Understanding AlphaFold Prediction with Explainable AI". In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599337.
Texto completo da fonteZhong, Bozitao, Xiaoming Su, Minhua Wen, Sicheng Zuo, Liang Hong e James Lin. "ParaFold: Paralleling AlphaFold for Large-Scale Predictions". In HPCAsia 2022 Workshop: International Conference on High Performance Computing in Asia-Pacific Region Workshops. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503470.3503471.
Texto completo da fonteChowdhury, Abu Sayeed, e Julia Thom Oxford. "Collagen a1 (XI) structure prediction by Alphafold 2". In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00108.
Texto completo da fonteSrivastava, Pragya, Shreyansh Suyash e N. Jayapandian. "Artificial Intelligence based System in Protein Folding using Alphafold". In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2022. http://dx.doi.org/10.1109/icacrs55517.2022.10029190.
Texto completo da fonteCheng, Shenggan, Xuanlei Zhao, Guangyang Lu, Jiarui Fang, Tian Zheng, Ruidong Wu, Xiwen Zhang, Jian Peng e Yang You. "FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters". In PPoPP '24: 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3627535.3638465.
Texto completo da fonteFujita, Hayato, Akihiro Nomura, Toshio Endo e Masakazu Sekijima. "Enhancing the Performance of AlphaFold Through Modified Storage Method and Optimization of HHblits on TSUBAME3.0 Supercomputer". In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE). IEEE, 2023. http://dx.doi.org/10.1109/csce60160.2023.00351.
Texto completo da fonteSánchez-Galan, Javier. "MACHINE LEARNING Y SUS APLICACIONES". In V Congreso de Investigación Desarrollo en Innovación de la Universidad Internacional de Ciencia y Tecnología. Universidad Internacional de Ciencia y Tecnología, 2021. http://dx.doi.org/10.47300/978-9962-5599-8-6-03.
Texto completo da fonteKonc, Janez, e Dušanka Janežič. "Algorithms and web servers for protein binding sites detection in drug discovery". In 2nd International Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.014k.
Texto completo da fonte