Auswahl der wissenschaftlichen Literatur zum Thema „Continuous conformational variability of biomolecules“
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Zeitschriftenartikel zum Thema "Continuous conformational variability of biomolecules"
Vuillemot, Rémi, Mohamad Harastani, Ilyes Hamitouche und Slavica Jonic. „MDSPACE and MDTOMO Software for Extracting Continuous Conformational Landscapes from Datasets of Single Particle Images and Subtomograms Based on Molecular Dynamics Simulations: Latest Developments in ContinuousFlex Software Package“. International Journal of Molecular Sciences 25, Nr. 1 (19.12.2023): 20. http://dx.doi.org/10.3390/ijms25010020.
Der volle Inhalt der QuelleLuchinat, Claudio. „Exploring the conformational heterogeneity of biomolecules: theory and experiments“. Physical Chemistry Chemical Physics 18, Nr. 8 (2016): 5684–85. http://dx.doi.org/10.1039/c6cp90029a.
Der volle Inhalt der QuelleDeVore, Kira, und Po-Lin Chiu. „Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity“. Biomolecules 12, Nr. 5 (24.04.2022): 628. http://dx.doi.org/10.3390/biom12050628.
Der volle Inhalt der QuelleSorzano, C. O. S., A. Jiménez, J. Mota, J. L. Vilas, D. Maluenda, M. Martínez, E. Ramírez-Aportela et al. „Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy“. Acta Crystallographica Section F Structural Biology Communications 75, Nr. 1 (01.01.2019): 19–32. http://dx.doi.org/10.1107/s2053230x18015108.
Der volle Inhalt der QuelleMa, Shaoqing, Zhiwei Li, Shixiang Gong, Chengbiao Lu, Xiaoli Li und Yingwei Li. „High Frequency Electromagnetic Radiation Stimulates Neuronal Growth and Hippocampal Synaptic Transmission“. Brain Sciences 13, Nr. 4 (19.04.2023): 686. http://dx.doi.org/10.3390/brainsci13040686.
Der volle Inhalt der QuelleValimehr, Sepideh, Rémi Vuillemot, Mohsen Kazemi, Slavica Jonic und Isabelle Rouiller. „Analysis of the Conformational Landscape of the N-Domains of the AAA ATPase p97: Disentangling the Continuous Conformational Variability in Partially Symmetrical Complexes“. International Journal of Molecular Sciences 25, Nr. 6 (16.03.2024): 3371. http://dx.doi.org/10.3390/ijms25063371.
Der volle Inhalt der QuellePancera, S. M., H. Gliemann, D. F. S. Petri und T. Schimmel. „Adsorption Behaviour of Creatine Phosphokinase onto Silicon Wafers: Comparison between Ellipsometric and Atomic Force Microscopy Data“. Microscopy and Microanalysis 11, S03 (Dezember 2005): 56–60. http://dx.doi.org/10.1017/s1431927605050889.
Der volle Inhalt der QuelleHarastani, Mohamad, Mikhail Eltsov, Amélie Leforestier und Slavica Jonic. „TomoFlow: Analysis of Continuous Conformational Variability of Macromolecules in Cryogenic Subtomograms based on 3D Dense Optical Flow“. Journal of Molecular Biology 434, Nr. 2 (Januar 2022): 167381. http://dx.doi.org/10.1016/j.jmb.2021.167381.
Der volle Inhalt der QuelleWang, Chenzheng, Yuexia Lin, Devin Bougie und Richard E. Gillilan. „Predicting data quality in biological X-ray solution scattering“. Acta Crystallographica Section D Structural Biology 74, Nr. 8 (24.07.2018): 727–38. http://dx.doi.org/10.1107/s2059798318005004.
Der volle Inhalt der QuelleMora-Navarro, Camilo, Mario E. Garcia, Prottasha Sarker, Emily W. Ozpinar, Jeffrey R. Enders, Saad Khan, Ryan C. Branski und Donald O. Freytes. „Monitoring decellularization via absorbance spectroscopy during the derivation of extracellular matrix scaffolds“. Biomedical Materials 17, Nr. 1 (26.11.2021): 015008. http://dx.doi.org/10.1088/1748-605x/ac361f.
Der volle Inhalt der QuelleDissertationen zum Thema "Continuous conformational variability of biomolecules"
Harastani, Mohamad. „Image analysis methods development for in vitro and in situ cryo-electron tomography studies of conformational variability of biomolecular complexes : Case of nucleosome structural and dynamics studies“. Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS283.
Der volle Inhalt der QuelleCryogenic electron tomography (cryo-ET) allows visualizing biomolecular complexes in situ. 3D data of biomolecules produced using cryo-ET are noisy, suffer from spacial anisotropies, and are difficult to analyze individually. Biomolecules are flexible, and analyzing their conformational variability is necessary to understand their functional mechanisms. Standard cryo-ET data processing methods average multiple copies of individual biomolecules to obtain structures at higher resolutions and consider that biomolecular conformational variability is discrete rather than continuous using the classification. This thesis presents the first two cryo-ET data processing methods for analyzing biomolecular continuous conformational variability, HEMNMA-3D and TomoFlow. HEMNMA-3D analyzes experimental data with the motion directions simulated by Normal Mode Analysis and allows the discovery of a large range of biomolecular motions. TomoFlow extracts motions from the data using the computer vision technique of Optical Flow. I show the potential of these two methods on experimental cryo-ET data of nucleosome conformational variability in cells. The two methods show coherent results, shedding light on the conformational variability of nucleosomes in cells
Hamitouche, Ilyes. „Machine learning for determining continuous conformational transitions of biomolecular complexes from single-particle cryo-electron microscopy images“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS047.
Der volle Inhalt der QuelleDuring my thesis, I developed three methods based on deep learning to extract continuous conformational variability of biomolecular complexes from single-particle cryo electron microscopy images. The following three methods are described in this thesis manuscript, along with their results on test data: supervised DeepHEMNMA, supervised Cryo-VIT, and unsupervised Cryo-VIT. DeepHEMNMA is a fast conformational space determination method that uses a convolutional neural network to accelerate a previously developed method for continuous conformational analysis, HEMNMA , which combines a motion simulation computed by normal mode analysis (NMA) with an image processing approach. In contrast to DeepHEMNMA, the Cryo-ViT approaches learn to match each image to a large number of atomic coordinates using a variational autoencoder
Konferenzberichte zum Thema "Continuous conformational variability of biomolecules"
Hamitouche, Ilyes, und Slavica Jonic. „Deep learning of elastic 3D shapes for cryo electron microscopy analysis of continuous conformational changes of biomolecules“. In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616013.
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