Littérature scientifique sur le sujet « Protein-RNA docking »
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Articles de revues sur le sujet "Protein-RNA docking"
Arnautova, Yelena A., Ruben Abagyan et Maxim Totrov. « Protein-RNA Docking Using ICM ». Journal of Chemical Theory and Computation 14, no 9 (17 juillet 2018) : 4971–84. http://dx.doi.org/10.1021/acs.jctc.8b00293.
Texte intégralHe, Jiahua, Huanyu Tao et Sheng-You Huang. « Protein-ensemble–RNA docking by efficient consideration of protein flexibility through homology models ». Bioinformatics 35, no 23 (14 mai 2019) : 4994–5002. http://dx.doi.org/10.1093/bioinformatics/btz388.
Texte intégralDelgado Blanco, Javier, Leandro G. Radusky, Damiano Cianferoni et Luis Serrano. « Protein-assisted RNA fragment docking (RnaX) for modeling RNA–protein interactions using ModelX ». Proceedings of the National Academy of Sciences 116, no 49 (15 novembre 2019) : 24568–73. http://dx.doi.org/10.1073/pnas.1910999116.
Texte intégralPérez-Cano, Laura, Miguel Romero-Durana et Juan Fernández-Recio. « Structural and energy determinants in protein-RNA docking ». Methods 118-119 (avril 2017) : 163–70. http://dx.doi.org/10.1016/j.ymeth.2016.11.001.
Texte intégralZhang, Zhao, Lin Lu, Yue Zhang, Chun Hua Li, Cun Xin Wang, Xiao Yi Zhang et Jian Jun Tan. « A combinatorial scoring function for protein-RNA docking ». Proteins : Structure, Function, and Bioinformatics 85, no 4 (9 février 2017) : 741–52. http://dx.doi.org/10.1002/prot.25253.
Texte intégralZheng, Jinfang, Xu Hong, Juan Xie, Xiaoxue Tong et Shiyong Liu. « P3DOCK : a protein–RNA docking webserver based on template-based and template-free docking ». Bioinformatics 36, no 1 (7 juin 2019) : 96–103. http://dx.doi.org/10.1093/bioinformatics/btz478.
Texte intégralSetny, Piotr, et Martin Zacharias. « A coarse-grained force field for Protein–RNA docking ». Nucleic Acids Research 39, no 21 (16 août 2011) : 9118–29. http://dx.doi.org/10.1093/nar/gkr636.
Texte intégralNithin, Chandran, Sunandan Mukherjee et Ranjit Prasad Bahadur. « A non-redundant protein-RNA docking benchmark version 2.0 ». Proteins : Structure, Function, and Bioinformatics 85, no 2 (2 décembre 2016) : 256–67. http://dx.doi.org/10.1002/prot.25211.
Texte intégralWicaksono, Adhityo, et Arli Aditya Parikesit. « Molecular Docking and Dynamics of SARS-CoV-2 Programmed Ribosomal Frameshifting RNA and Ligands for RNA-Targeting Alkaloids Prospecting ». HAYATI Journal of Biosciences 30, no 6 (24 juillet 2023) : 1025–35. http://dx.doi.org/10.4308/hjb.30.6.1025-1035.
Texte intégralLi, Yaozong, Jie Shen, Xianqiang Sun, Weihua Li, Guixia Liu et Yun Tang. « Accuracy Assessment of Protein-Based Docking Programs against RNA Targets ». Journal of Chemical Information and Modeling 50, no 6 (19 mai 2010) : 1134–46. http://dx.doi.org/10.1021/ci9004157.
Texte intégralThèses sur le sujet "Protein-RNA docking"
Kravchenko, Anna. « Fragment-based modelling of protein-RNA complexes for protein design ». Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0370.
Texte intégralProtein-RNA complexes play crucial roles in cell regulation. Predicting their 3D structure has applications in protein design and drug development. The ITN project RNAct aimed to combine experimental and computational methods to design new "RNA recognition motifs" (RRM) - protein domains interacting with single-stranded RNA (ssRNA) - for applications in synthetic biology and bioanalysis. Modelling protein-ssRNA complexes (docking) is an arduous task due to the flexibility of ssRNA, which lacks a proper structure in its free form. Traditional docking methods sample the relative positions (poses) of 2 molecular structures and score them to select the correct (near-native) ones. It is not directly applicable here due to the absence of free ssRNA structures, nor is deep learning due to the too low number of known structures for training. Fragment-based docking (FBD), the state-of-the-art approach for ssRNA, docks all possible conformations of RNA fragments onto a protein and assembles their best-scored poses combinatorially. ssRNA'TTRACT, our FBD method, uses the well-known ATTRACT docking software, with its coarse-grained representation that replaces atom groups by one bead. Yet the RNA-protein parameters of ATTRACT scoring function (ASF) are not ssRNA-specific and require optimisation. Additionally, RRM-specific features can be learned and used to guide the docking. With my colleague H. Dhondge, we have developed a data-driven FBD pipeline for RRM-ssRNA complexes, as an updated version of an existing strategy. RRMs have two aromatic amino acids (aa) in conserved positions, each stacking with a nucleotide of the bound ssRNA. H. Dhondge collected all known RRM-ssRNA structures with such stacking and clustered them to obtain a set of prototypes for the 3D coordinates of such interactions in RRM. I then set up a docking pipeline with as input the RRM and RNA sequences and the identification of the stacked nucleotides. The pipeline retrieves the RRM structure from AlphaFoldDB, identifies possible 3D positions of the stacked nucleotides and runs ssRNA'TTRACT with maximal distance restraints toward each position. In parallel, we addressed the weakness of ASF for ssRNA by deriving HIPPO (HIstogram-based Pseudo-POtential), a new scoring potential for ATTRACT poses of ssRNA on RRM, based on the frequency of bead-bead distances in near-native versus wrong poses. It combines 4 distinct parameter sets (four Η) into a consensus scoring, to better account for the diverse RRM-ssRNA binding modes. Tested in a leave-one-out approach, HIPPO reaches a 3-fold enrichment of near-natives in 20% top-scored poses for ½ of the ssRNA fragments, versus ¼ with ASF. It even reaches a 4-fold enrichment for ⅓ of the fragments, versus 7% of the fragments with ASF. Surprisingly, HIPPO performed better than ASF also on a benchmark of non-RRM proteins, while trained only on RRMs. Most FBD approaches encounter inherent scoring issues, probably due to some fragments binding more specifically/strongly than others. To address this point, we examined the best-scored fragment per complex and found that HIPPO consistently selects more near-natives than ASF for this fragment. This inspired an incremental docking approach: the top-ranked poses of one fragment are used as a starting point to build a full RNA chain incrementally. This strategy eliminates the need for known conserved contacts, which have been required so far to obtain accurate models, making it generalizable to non-RRM proteins. Future research aims to identify the best-performing Η for each fragment, potentially using (deep) machine learning. Our workflow to derive scoring parameters is in principle applicable to any protein/ligand type and we plan to expand it to other RNA-binding protein domains, as well as ssDNA and long peptides
Patschull, Lafitte-Laplace Anathe Olivia Maria. « In silico ligand fitting/docking, computational analysis and biochemical/biophysical validation for protein-RNA recognition and for rational drug design in diseases ». Thesis, Birkbeck (University of London), 2014. http://bbktheses.da.ulcc.ac.uk/84/.
Texte intégralChevrollier, Nicolas. « Développement et application d’une approche de docking par fragments pour modéliser les interactions entre protéines et ARN simple-brin ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS106/document.
Texte intégralRNA-protein interactions mediate numerous fundamental cellular processes. Atomic scale details of these interactions shed light on their functions but can also allow the rational design of ligands that could modulate them. NMR and X-ray crystallography are the 2 main techniques used to resolve 3D highresolution structures between two interacting molecules. Docking approaches can also be utilized to give models as an alternative. However, the application of these approaches to RNA-protein complexes is hampered by an issue. RNA-protein interactions often relies on the specific recognition of a short singlestranded RNA (ssRNA) sequence by the protein. The inherent flexibility of the ssRNA segment would impose, in a classical docking approach, to explore their resulting large conformation space which is not computationally reliable. The goal of this project is to overcome this barrier by using a fragment-based docking approach. This approach developed from some of the most represented RNA-binding domains showed excellent results in the prediction of the ssRNA-protein binding mode from the RNA sequence and also a great potential to predict preferential RNA binding sequences
Zhang, Jin. « Macromolecular Interactions in West Nile Virus RNA-TIAR Protein Complexes and of Membrane Associated Kv Channel Peptides ». Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/chemistry_diss/81.
Texte intégralChapitres de livres sur le sujet "Protein-RNA docking"
Madan, Bharat, Joanna M. Kasprzak, Irina Tuszynska, Marcin Magnus, Krzysztof Szczepaniak, Wayne K. Dawson et Janusz M. Bujnicki. « Modeling of Protein–RNA Complex Structures Using Computational Docking Methods ». Dans Methods in Molecular Biology, 353–72. New York, NY : Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3569-7_21.
Texte intégralGuo, Yun, Xiaoyong Pan et Hong-Bin Shen. « Recent progress of methodology development for protein–RNA docking ». Dans Protein Interactions, 271–95. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811211874_0011.
Texte intégralUmare, Mohit, Fai A. Alkathiri et Rupesh Chikhale. « Development of Nucleic Acid Targeting Molecules : Molecular Docking Approaches and Recent Advances ». Dans Biomedical Engineering. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.107349.
Texte intégralPÉREZ-CANO, LAURA, ALBERT SOLERNOU, CARLES PONS et JUAN FERNÁNDEZ-RECIO. « STRUCTURAL PREDICTION OF PROTEIN-RNA INTERACTION BY COMPUTATIONAL DOCKING WITH PROPENSITY-BASED STATISTICAL POTENTIALS ». Dans Biocomputing 2010, 293–301. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295291_0031.
Texte intégralMadala, Sanjay, S. S. V. Kiran K et Burra V. L. S. Prasad. « In Silico Design of Natural Compound-Derived Novel Inhibitors Against RdRP OF SARS-CoV-2 ». Dans Current Trends in Drug Discovery, Development and Delivery (CTD4-2022), 142–54. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781837671090-00142.
Texte intégralOxford, John, Paul Kellam et Leslie Collier. « General properties of viruses ». Dans Human Virology. Oxford University Press, 2016. http://dx.doi.org/10.1093/hesc/9780198714682.003.0002.
Texte intégralActes de conférences sur le sujet "Protein-RNA docking"
Kralj, Sebastjan, Milan Hodošček, Marko Jukić et Urban Bren. « A comprehensive in silico protocol for fast automated mutagenesis and binding affinity scoring of protein-ligand complexes ». Dans 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.674k.
Texte intégralSan, Avdar, Anjana Saxena et Shaneen Singh. « Abstract 844 : RNA binding domains of nucleolin exhibit specificity in driving nucleolin-miRNA interactions : Anin silicomodeling and RNA-protein docking study ». Dans Proceedings : AACR Annual Meeting 2020 ; April 27-28, 2020 and June 22-24, 2020 ; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-844.
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