Relevant bibliographies by topics / Protein dynamic

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Protein dynamic'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Protein dynamic"

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Rackovsky, S., and Harold A. Scheraga. "The structure of protein dynamic space." Proceedings of the National Academy of Sciences 117, no. 33 (2020): 19938–42. http://dx.doi.org/10.1073/pnas.2008873117.

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We use a bioinformatic description of amino acid dynamic properties, based on residue-specific average B factors, to construct a dynamics-based, large-scale description of a space of protein sequences. We examine the relationship between that space and an independently constructed, structure-based space comprising the same sequences. It is demonstrated that structure and dynamics are only moderately correlated. It is further shown that helical proteins fall into two classes with very different structure–dynamics relationships. We suggest that dynamics in the two helical classes are dominated b
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Zheng, Li-E., Shrishti Barethiya, Erik Nordquist, and Jianhan Chen. "Machine Learning Generation of Dynamic Protein Conformational Ensembles." Molecules 28, no. 10 (2023): 4047. http://dx.doi.org/10.3390/molecules28104047.

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Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling,
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Helmerhorst, E. J., and F. G. Oppenheim. "Saliva: a Dynamic Proteome." Journal of Dental Research 86, no. 8 (2007): 680–93. http://dx.doi.org/10.1177/154405910708600802.

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The proteome of whole saliva, in contrast to that of serum, is highly susceptible to a variety of physiological and biochemical processes. First, salivary protein secretion is under neurologic control, with protein output being dependent on the stimulus. Second, extensive salivary protein modifications occur in the oral environment, where a plethora of host- and bacteria-derived enzymes act on proteins emanating from the glandular ducts. Salivary protein biosynthesis starts with the transcription and translation of salivary protein genes in the glands, followed by post-translational processing
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Zhang, Jinxiong, Cheng Zhong, Hai Xiang Lin, and Mian Wang. "Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks." BioMed Research International 2019 (August 21, 2019): 1–17. http://dx.doi.org/10.1155/2019/3726721.

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Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene e
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Franklin, J., and S. Doniach. "Dynamic bond constraints in protein Langevin dynamics." Journal of Chemical Physics 124, no. 15 (2006): 154901. http://dx.doi.org/10.1063/1.2178325.

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Guo, Dongliang, Li Feng, Chuanbao Shi, et al. "VAPPD: Visual Analysis of Protein Pocket Dynamics." Applied Sciences 12, no. 20 (2022): 10465. http://dx.doi.org/10.3390/app122010465.

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Analyzing the intrinsic dynamic characteristics of protein pockets is a key aspect to understanding the functional mechanism of proteins, which is conducive to the discovery and development of drugs. At present, the research on the dynamic characteristics of pockets mainly focuses on pocket stability, similarity, and physicochemical properties. However, due to the high complexity and diversity of high-dimensional pocket data in dynamic processes, this work is challenging. In this paper, we explore the dynamic characteristics of protein pockets based on molecular dynamics (MD) simulation trajec
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Taylor, Susan S., and Alexandr P. Kornev. "Protein kinases: evolution of dynamic regulatory proteins." Trends in Biochemical Sciences 36, no. 2 (2011): 65–77. http://dx.doi.org/10.1016/j.tibs.2010.09.006.

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Cheng, Kaihui, Ce Liu, Qingkun Su, et al. "4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 93–101. https://doi.org/10.1609/aaai.v39i1.31984.

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Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the fo
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Kirk, Rebecca. "Dynamic protein structures." Nature Methods 12, S1 (2015): 18. http://dx.doi.org/10.1038/nmeth.3522.

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Kharchenko, Vladlena, Michal Nowakowski, Mariusz Jaremko, Andrzej Ejchart, and Łukasz Jaremko. "Dynamic 15N{1H} NOE measurements: a tool for studying protein dynamics." Journal of Biomolecular NMR 74, no. 12 (2020): 707–16. http://dx.doi.org/10.1007/s10858-020-00346-6.

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AbstractIntramolecular motions in proteins are one of the important factors that determine their biological activity and interactions with molecules of biological importance. Magnetic relaxation of 15N amide nuclei allows one to monitor motions of protein backbone over a wide range of time scales. 15N{1H} nuclear Overhauser effect is essential for the identification of fast backbone motions in proteins. Therefore, exact measurements of NOE values and their accuracies are critical for determining the picosecond time scale of protein backbone. Measurement of dynamic NOE allows for the determinat
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Dissertations / Theses on the topic "Protein dynamic"

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Munz, Marton. "Computational studies of protein dynamics and dynamic similarity." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:2fb76765-3e43-409b-aad3-b5202f4668b3.

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At the time of writing this thesis, the complete genomes of more than 180 organisms have been sequenced and more than 80000 biological macromolecular structures are available in the Protein Data Bank (PDB). While the number of sequenced genomes and solved three-dimensional structures are rapidly increasing, the functional annotation of protein sequences and structures is a much slower process, mostly because the experimental de-termination of protein function is expensive and time-consuming. A major class of in silico methods used for protein function prediction aim to transfer annotations bet
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Wallach, Thomas. "A dynamic circadian protein-protein interaction network." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16604.

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Die dynamische Regulation von Protein-Protein Interaktionen (PPIs) ist wichtig für den Ablauf von biologischen Prozessen. Die circadiane Uhr, die einen ~24 Stunden Rhythmus generiert und eine Vielzahl von physiologischen Parametern steuert kann auch die Dynamik von PPIs regulieren. Um neue Erkenntnisse über regulatorische Mechanismen innerhalb des molekularen Oszillators zu gewinnen, habe ich zunächst alle möglichen PPIs zwischen 46 circadianen Komponenten mittels eines systematischen yeast-two-hybid (Y2H) Screens bestimmt. Dabei habe ich 109 bis dahin noch unbekannte PPIs identifiziert und
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Bhat, Venugopal T. "Protein-directed dynamic combinatorial chemistry." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/8758.

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Dynamic combinatorial chemistry (DCC) is a novel approach to medicinal chemistry which integrates the synthesis and screening of small molecule libraries into a single step. The concept uses reversible chemical reactions to present a dynamic library of candidate structures to a template which selects and removes the best binder from equilibrium. Using this evolutionary process with a biopolymer template, such as a protein, leads to the protein directing the synthesis of its own best ligand. Biological DCC applications are extremely challenging since the thermodynamic criterion of reversibility
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Gallia, Jason. "Protein identification by dynamic programming." Diss., Online access via UMI:, 2009.

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Jochi, Yasumasa. "Crystallographic Refinement of Protein Dynamic Structure." 京都大学 (Kyoto University), 2002. http://hdl.handle.net/2433/150011.

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Rose, Alexander. "The dynamic coupling interface of G-protein coupled receptors." Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17215.

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Um mit ihrer Umgebung zu kommunizieren verfügen lebende Zellen über Rezeptoren, welche die umschließende Membran überbrücken. Die vorherrschende G-Protein-gekoppelte Rezeptoren (GPCR) erhalten Informationen von Außerhalb durch Bindung eines Liganden, wodurch der Rezeptor aktiviert wird. Während der Aktivierung bildet sich innerzellulär ein offener Spalt, in den ein G-Protein (Gαβγ, G) mit seinem C-terminalen Ende koppeln kann. Die Bindung an einen GPCR führt in der Gα-Untereinheit vom Gαβγ zu einen GDP/GTP-Austausch, welcher für die weitere Signalübertragung ins Zellinnere notwendig ist. Die K
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Beveridge, Rebecca. "Mass spectrometry methods for characterising the dynamic behaviour of proteins and protein complexes." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/mass-spectrometry-methods-for-characterising-the-dynamic-behaviour-of-proteins-and-protein-complexes(81961313-2d3e-4ad3-9c6f-6299549e9738).html.

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Research into the relationship between the structure and function of proteins has been ongoing now for several decades. More recently, there has been an explosion in the investigation of the dynamic properties of proteins, and how their dynamic propensity relates to their function. This new direction in protein research requires new techniques to analyse protein dynamics, since most traditional techniques are biased towards a fixed tertiary structure. Mass spectrometry (MS) is emerging as a powerful tool to probe protein dynamics since it can provide information on interconverting conformation
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Lock, John George. "Dynamic imaging of post-Golgi protein transport /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19397.pdf.

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Reeh, Philipp. "Dynamic Multivalency For The Recognition Of Protein Surfaces." Doctoral thesis, Universitat Rovira i Virgili, 2014. http://hdl.handle.net/10803/283236.

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En esta tesis doctoral el concepto de multivalencia en el reconocimiento de proteínas (lectinas) con azúcares se combinó con la idea de la química dinámica combinatoria. Esto se aplicó, no sólo para sacar ventaja del efecto de la mejor afinidad de tales sistemas multivalentes, sino también para dotar al sistema con una mayor variedad de constituciones y geometrías. La determinación de las afinidades relativas de los miembros de la biblioteca dinámica dio una visión de los requisitos necesarios para la unión entre azúcares – lectina. El primer enfoque para acceder a los sistemas multivalent
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Carter, Nathan Andrew. "Design Strategies for Dynamic Self-assembled Protein Materials." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/93207.

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Structures in nature exhibit unique and complex architectures whose order propagates from nano- (10-9 m) to macro-scales (mm to m). These structures give rise to a rich diversity of adaptive function that allows for life in all environments on Earth. This complex functionality has driven research into bio-inspired materials where scientists investigate the complex relationship between sequence, structure and function of these materials. A good illustrative example of the effect that hierarchical structure can have is a brick wall. Bricks are laid so that the layer on top is shifted in either d
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Books on the topic "Protein dynamic"

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Livesay, Dennis R., ed. Protein Dynamics. Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-658-0.

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A, Eaton William, and Szabo Attila 1947-, eds. Protein dynamics. North-Holland, 1991.

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Han, Ke-li, Xin Zhang, and Ming-jun Yang, eds. Protein Conformational Dynamics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02970-2.

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Livesay, Dennis R. Protein dynamics: Methods and protocols. Humana Press, 2013.

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Vaidehi, Nagarajan, and Judith Klein-Seetharaman, eds. Membrane Protein Structure and Dynamics. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6.

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Jardetzky, Oleg, Jean-François Lefèvre, and Robin E. Holbrook, eds. Protein Dynamics, Function, and Design. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4895-9.

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Oleg, Jardetzky, Lefèvre Jean-François, Holbrook Robin, and NATO Advanced Study Institute and International School of Structural Biology and Magnetic Resonance, 3rd Course on Protein Dynamics, Function, and Design (1997 : Erice, Italy), eds. Protein dynamics, function, and design. Plenum Press, 1998.

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Jardetzky, Oleg. Protein Dynamics, Function, and Design. Springer US, 1998.

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Saudagar, Prakash, and Timir Tripathi, eds. Protein Folding Dynamics and Stability. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2079-2.

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1995), Cold Spring Harbor Symposium on Quantitative Biology (60th. Protein kinesis: The dynamics of protein trafficking and stability. Cold Spring Harbor Laboratory Press, 1995.

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Book chapters on the topic "Protein dynamic"

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Cheng, Chi-Yuan, Jinsuk Song, John M. Franck, and Songi Han. "Mapping Out Protein Hydration Dynamics by Overhauser Dynamic Nuclear Polarization." In Protein NMR. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7621-5_2.

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Schäffler, Andreas, and Thomas Karrasch. "Protein and Nucleotide Metabolism." In Dynamic Endocrine Testing. Springer Berlin Heidelberg, 2024. https://doi.org/10.1007/978-3-662-70260-4_4.

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Oliver, Robin, Robin A. Richardson, Ben Hanson, et al. "Modelling the Dynamic Architecture of Biomaterials Using Continuum Mechanics." In Protein Modelling. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09976-7_8.

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Han, Kyungsook, Byong-Hyon Ju, and Jong H. Park. "InterViewer: Dynamic Visualization of Protein-Protein Interactions." In Graph Drawing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36151-0_35.

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Mittermaier, Anthony, and Erick Meneses. "Analyzing Protein–Ligand Interactions by Dynamic NMR Spectroscopy." In Protein-Ligand Interactions. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-398-5_9.

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Mccallum, Matthew, Lori L. Burrows, and P. Lynne Howell. "The Dynamic Structures of the Type IV Pilus." In Protein Secretion in Bacteria. ASM Press, 2019. http://dx.doi.org/10.1128/9781683670285.ch10.

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Corbett, Daniel, Jordan W. Bye, and Robin A. Curtis. "Measuring Nonspecific Protein–Protein Interactions by Dynamic Light Scattering." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9678-0_1.

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Zhao, Bihai, Jianxin Wang, Fang-Xiang Wu, and Yi Pan. "Predicting Protein Functions Based on Dynamic Protein Interaction Networks." In Bioinformatics Research and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19048-8_33.

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Petroll, W. Matthew. "Dynamic Assessment of Cell-Matrix Mechanical Interactions in Three-Dimensional Culture." In Adhesion Protein Protocols. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-353-0_6.

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Stanković, Bratislav, Amy Clore, Shunnosuke Abe, Brian Larkins, and Eric Davies. "Actin in Protein Synthesis and Protein Body Formation." In Actin: A Dynamic Framework for Multiple Plant Cell Functions. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9460-8_8.

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Conference papers on the topic "Protein dynamic"

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Chen, Yaoran, Difu Feng, Yuanyuan Zhu, et al. "DCOAT: Dynamic Core-attachment based Protein Complex Detection." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821746.

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Parpinelli, Rafael Stubs, Nicholas Wojeicchowski, and Nilcimar Neitzel Will. "Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information." In 2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2024. http://dx.doi.org/10.1109/cibcb58642.2024.10702172.

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Acosta-Pavas, Juan Camilo, David Camilo Corrales, Susana Mar�a Alonso Villela, et al. "Learning-based Control Approach for Nanobody-scorpion Antivenom Optimization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.149893.

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One market scope of bioindustries is the production of recombinant proteins for its application in serotherapy. However, its process's monitoring and optimization present limitations. There are different approaches to optimize bioprocess performance; one is using model-based control strategies such as Model Predictive Control (MPC). Another strategy is learning-based control, such as Reinforcement Learning (RL). In this work, an RL approach was applied to maximize the production of recombinant proteins in E. coli at the�induction phase using as a control variable the substrate feed flow rate (
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Wang, Ling, Xiao Yan Xia, Tie Hua Zhou, and Wan Lin Zhang. "Dynamic Reachability Distance-Based Dense Clustering Method for Protein-DNA Sites Noise Reduction Based on the Predicted Binding Site Probabilities." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033446.

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Petre, Irina-Oana. "Rb protein dynamic modeling." In 2017 E-Health and Bioengineering Conference (EHB). IEEE, 2017. http://dx.doi.org/10.1109/ehb.2017.7995485.

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Ameer-Beg, Simon M., Marion Peter, Melanie D. Keppler, et al. "Dynamic imaging of protein-protein interactions by MP-FLIM." In Biomedical Optics 2005, edited by Ammasi Periasamy and Peter T. C. So. SPIE, 2005. http://dx.doi.org/10.1117/12.589202.

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Jie Zhao, Xiujuan Lei, and Fang-Xiang Wu. "Identifying protein complexes in dynamic protein-protein interaction networks based on Cuckoo Search algorithm." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822704.

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Fu, Dongqi, and Jingrui He. "DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020904.

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Guo, Yang, Xuequn Shang, Jing Li, and Zhanhuai Li. "Revealing the Causes of Dynamic Change in Protein-Protein Interaction Network." In 2013 IEEE International Congress on Big Data (BigData Congress). IEEE, 2013. http://dx.doi.org/10.1109/bigdata.congress.2013.33.

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Fan, Z. Hugh, Champak Das, Cesar Moreira, Daniel Olivero, and Hong Chen. "Microfluidic Devices for Rapid Protein Separation." In 2008 Second International Conference on Integration and Commercialization of Micro and Nanosystems. ASMEDC, 2008. http://dx.doi.org/10.1115/micronano2008-70208.

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We present our investigation of using microfluidic devices for rapid protein separation. The devices were made from cyclic olefin copolymers that have high optical clarity and high glass transition temperature. Protein separation was achieved by using isoelectric focusing (IEF) and polyacrylamide gel electrophoresis (PAGE). A laser-induced fluorescence (LIF) imaging system was developed to detect proteins while they migrated under an electric field. IEF was carried out in a separation medium consisting of carrier ampholytes and a mixture of linear polymers. Dynamic coating of the linear polyme
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Reports on the topic "Protein dynamic"

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Miller, Susan M. Structure/Function Analysis of Protein-Protein Interactions and Role of Dynamic Motions in Mercuric Ion Reductase. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/840156.

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Horwitz, Andrew A. BRCA1 Protein Complexes: Dynamic Changes and Functions Important in Breast Cancer. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada436903.

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Horwitz, Andrew. BRCA1 Protein Complexes: Dynamic Changes and Functions Important in Breast Cancer. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada455789.

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Horwitz, Andrew. BRCA1 Protein Complexes: Dynamic Changes and Functions Important in Breast Cancer. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada470582.

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Lee, Andrew Loyd. Structural and dynamic characterization of eukaryotic gene regulatory protein domains in solution. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/373861.

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Asenath-Smith, Emily, Emily Jeng, Emma Ambrogi, Garrett Hoch, and Jason Olivier. Investigations into the ice crystallization and freezing properties of the antifreeze protein ApAFP752. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45620.

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Antifreeze proteins (AFPs) allow biological organisms, including insects, fish, and plants, to survive in freezing temperatures. While in solution, AFPs impart cryoprotection by creating a thermal hysteresis (TH), imparting ice recrystallization inhibition (IRI), and providing dynamic ice shaping (DIS). To leverage these ice-modulating effects of AFPs in other scenarios, a range of icing assays were performed with AFPs to investigate how AFPs interact with ice formation when tethered to a surface. In this work, we studied ApAFP752, an AFP from the beetle Anatolica polita, and first investigate
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McAdams, Harley, Lucille Shapiro, Mark Horowitz, et al. Dynamic spatial organization of multi-protein complexes controlling microbial polar organization, chromosome replication, and cytokinesis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1043294.

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Marrogi, Eryney, and Theodore Sternlieb. How Prions Can Unlock Protein Design. Asimov Press, 2025. https://doi.org/10.62211/54tr-21po.

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Scavuzzo, Sebastian, Jonathan Cedeño, and Jaroslava Miksovska. In Silico Calculation of Interhelical Angles in NCS1. Florida International University, 2025. https://doi.org/10.25148/fiuurj.3.1.16.

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Proteins are important macromolecules responsible for a variety of processes in living organisms. One of the most important features of proteins is their ability to respond to environmental stimuli, such as changes in intracellular metal concentration by binding metal ions, which in turns triggers structural changes within the protein that can modify its function or allow the protein to participate in a signaling pathway. One such signaling protein is the so-called neuronal calcium sensor 1 protein or NCS1, which binds Ca2+ along with other abiogenic metals such as Li+, and the metal binding r
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Chamovitz, Daniel, and Albrecht Von Arnim. Translational regulation and light signal transduction in plants: the link between eIF3 and the COP9 signalosome. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7696515.bard.

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The COP9 signalosome (CSN) is an eight-subunit protein complex that is highly conserved among eukaryotes. Genetic analysis of the signalosome in the plant model species Arabidopsis thaliana has shown that the signalosome is a repressor of light dependent seedling development as mutant Arabidopsis seedlings that lack this complex develop in complete darkness as if exposed to light. These mutant plants die following the seedling stage, even when exposed to light, indicating that the COP9 signalosome also has a central role in the regulation of normal photomorphogenic development. The biochemical
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