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Journal articles on the topic 'Protein dynamic'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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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4

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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5

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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6

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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7

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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8

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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Nicolau, Dan V., Kevin Burrage, Robert G. Parton, and John F. Hancock. "Identifying Optimal Lipid Raft Characteristics Required To Promote Nanoscale Protein-Protein Interactions on the Plasma Membrane." Molecular and Cellular Biology 26, no. 1 (2006): 313–23. http://dx.doi.org/10.1128/mcb.26.1.313-323.2006.

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ABSTRACT The dynamic lateral segregation of signaling proteins into microdomains is proposed to facilitate signal transduction, but the constraints on microdomain size, mobility, and diffusion that might realize this function are undefined. Here we interrogate a stochastic spatial model of the plasma membrane to determine how microdomains affect protein dynamics. Taking lipid rafts as representative microdomains, we show that reduced protein mobility in rafts segregates dynamically partitioning proteins, but the equilibrium concentration is largely independent of raft size and mobility. Rafts

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12

Tokunaga, Yuji, Thibault Viennet, Haribabu Arthanari, and Koh Takeuchi. "Spotlight on the Ballet of Proteins: The Structural Dynamic Properties of Proteins Illuminated by Solution NMR." International Journal of Molecular Sciences 21, no. 5 (2020): 1829. http://dx.doi.org/10.3390/ijms21051829.

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Solution NMR spectroscopy is a unique and powerful technique that has the ability to directly connect the structural dynamics of proteins in physiological conditions to their activity and function. Here, we summarize recent studies in which solution NMR contributed to the discovery of relationships between key dynamic properties of proteins and functional mechanisms in important biological systems. The capacity of NMR to quantify the dynamics of proteins over a range of time scales and to detect lowly populated protein conformations plays a critical role in its power to unveil functional prote

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13

Ulucan, Ozlem, Susanne Eyrisch, and Volkhard Helms. "Druggability of Dynamic Protein-protein Interfaces." Current Pharmaceutical Design 18, no. 30 (2012): 4599–606. http://dx.doi.org/10.2174/138161212802651652.

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14

O'Grad, Megan L., and Kevin Kit Parker. "Dynamic Control of Protein−Protein Interactions." Langmuir 24, no. 1 (2008): 316–22. http://dx.doi.org/10.1021/la702041g.

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15

Roberts, G. C. K. "Folding and unfolding for binding: large-scale protein dynamics in protein–protein interactions." Biochemical Society Transactions 34, no. 5 (2006): 971–74. http://dx.doi.org/10.1042/bst0340971.

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The role of dynamics in the function of proteins, from enzymes to signalling proteins, is widely recognized. In many cases, the dynamic process is a relatively localized one, involving motion of a limited number of key residues, while in others large-scale domain movements may be involved. These motions all take place within the context of a folded protein; however, there is increasing evidence for the existence of some proteins where a transition between folded and unfolded structures is required for function.

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Kmiecik, Sebastian, Maksim Kouza, Aleksandra Badaczewska-Dawid, Andrzej Kloczkowski, and Andrzej Kolinski. "Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models." International Journal of Molecular Sciences 19, no. 11 (2018): 3496. http://dx.doi.org/10.3390/ijms19113496.

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Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte C

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Saha, Sovan, Abhimanyu Prasad, Piyali Chatterjee, Subhadip Basu, and Mita Nasipuri. "Protein function prediction from dynamic protein interaction network using gene expression data." Journal of Bioinformatics and Computational Biology 17, no. 04 (2019): 1950025. http://dx.doi.org/10.1142/s0219720019500252.

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Computational prediction of functional annotation of proteins is an uphill task. There is an ever increasing gap between functional characterization of protein sequences and deluge of protein sequences generated by large-scale sequencing projects. The dynamic nature of protein interactions is frequently observed which is mostly influenced by any new change of state or change in stimuli. Functional characterization of proteins can be inferred from their interactions with each other, which is dynamic in nature. In this work, we have used a dynamic protein–protein interaction network (PPIN), time

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Straub, Sebastian P., Sebastian B. Stiller, Nils Wiedemann, and Nikolaus Pfanner. "Dynamic organization of the mitochondrial protein import machinery." Biological Chemistry 397, no. 11 (2016): 1097–114. http://dx.doi.org/10.1515/hsz-2016-0145.

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Abstract Mitochondria contain elaborate machineries for the import of precursor proteins from the cytosol. The translocase of the outer mitochondrial membrane (TOM) performs the initial import of precursor proteins and transfers the precursors to downstream translocases, including the presequence translocase and the carrier translocase of the inner membrane, the mitochondrial import and assembly machinery of the intermembrane space, and the sorting and assembly machinery of the outer membrane. Although the protein translocases can function as separate entities in vitro, recent studies revealed

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Vogel, Frank, Carsten Bornhövd, Walter Neupert, and Andreas S. Reichert. "Dynamic subcompartmentalization of the mitochondrial inner membrane." Journal of Cell Biology 175, no. 2 (2006): 237–47. http://dx.doi.org/10.1083/jcb.200605138.

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The inner membrane of mitochondria is organized in two morphologically distinct domains, the inner boundary membrane (IBM) and the cristae membrane (CM), which are connected by narrow, tubular cristae junctions. The protein composition of these domains, their dynamics, and their biogenesis and maintenance are poorly understood at the molecular level. We have used quantitative immunoelectron microscopy to determine the distribution of a collection of representative proteins in yeast mitochondria belonging to seven major processes: oxidative phosphorylation, protein translocation, metabolite exc

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Garbett, Damien, and Anthony Bretscher. "The surprising dynamics of scaffolding proteins." Molecular Biology of the Cell 25, no. 16 (2014): 2315–19. http://dx.doi.org/10.1091/mbc.e14-04-0878.

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The function of scaffolding proteins is to bring together two or more proteins in a relatively stable configuration, hence their name. Numerous scaffolding proteins are found in nature, many having multiple protein–protein interaction modules. Over the past decade, examples of scaffolding complexes long thought to be stable have instead been found to be surprisingly dynamic. These studies are scattered among different biological systems, and so the concept that scaffolding complexes might not always represent stable entities and that their dynamics can be regulated has not garnered general att

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21

Scholtens, Denise, and Robert Gentleman. "Making Sense of High-Throughput Protein-Protein Interaction Data." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (2005): 1–31. http://dx.doi.org/10.2202/1544-6115.1107.

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Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph-theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A likelihood-based objective function guides the protein complex modeling algorithm. With

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22

Chirgadze, Y. N., I. V. Likhachev, N. K. Balabaev та E. V. Brazhnikov. "Molecular Dynamics of Twisted β-Sheet inside a Protein Dimer of TTHA1013 Thermus thermophilus". Mathematical Biology and Bioinformatics 20, № 1 (2025): 47–53. https://doi.org/10.17537/2025.20.47.

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α-Helix and β-sheet are two basic secondary structure motifs of protein molecules. A true native protein state is realized in a water solution. Under these conditions the proteins carry out the dynamic properties and, what is important, fulfill their various functions. We recently studied molecular dynamics of α-helical fragment of the T. thermophilus TTHA1013 protein in a water solution. Here we consider dynamics of β-sheet in the dimer of this protein. Dynamics of only main chain Cα-atoms were studied. One of the most distinctive type of fluctuations was detected along four independent dynam

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23

Li, Min, Weijie Chen, Jianxin Wang, Fang-Xiang Wu, and Yi Pan. "Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/375262.

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Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expressi

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24

Boutagy, Nabil E., and William C. Sessa. "Dynamic Protein Palmitoylation Cycling." Circulation Research 127, no. 2 (2020): 266–68. http://dx.doi.org/10.1161/circresaha.120.317113.

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25

Li, Xiao-Han, Pavithra L. Chavali, and M. Madan Babu. "Capturing dynamic protein interactions." Science 359, no. 6380 (2018): 1105–6. http://dx.doi.org/10.1126/science.aat0576.

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26

M.D., Richard A. Stein,. "More Dynamic Protein Profiling." Genetic Engineering & Biotechnology News 35, no. 15 (2015): 14, 16–18. http://dx.doi.org/10.1089/gen.35.15.07.

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27

Yang, Lee Wei, and Shun Sakuraba. "Dynamic Predispositions of Protein-Protein Docking Orientation Revealed by Intrinsic Dynamic Domains." Biophysical Journal 98, no. 3 (2010): 238a. http://dx.doi.org/10.1016/j.bpj.2009.12.1289.

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28

Catez, Frédéric, Jae-Hwan Lim, Robert Hock, Yuri V. Postnikov, and Michael Bustin. "HMGN dynamics and chromatin function." Biochemistry and Cell Biology 81, no. 3 (2003): 113–22. http://dx.doi.org/10.1139/o03-040.

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Recent studies indicate that most nuclear proteins, including histone H1 and HMG are highly mobile and their interaction with chromatin is transient. These findings suggest that the structure of chromatin is dynamic and the protein composition at any particular chromatin site is not fixed. Here we discuss how the dynamic behavior of the nucleosome binding HMGN proteins affects the structure and function of chromatin. The high intranuclear mobility of HMGN insures adequate supply of protein throughout the nucleus and serves to target these proteins to their binding sites. Transient interactions

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Bharathwaj, J. Deepak Venkataraman N.* Charumathi P. Lakshminarasimman S. Purushothaman V. M. Sudharsan S. "An Overview of Basics, Types, Approaches, Applications, Advantages and Disadvantages of Docking." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 437–45. https://doi.org/10.5281/zenodo.14992121.

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Background-Molecular docking serves as an effective method for exploring the molecular targets of nutraceuticals in the treatment of diseases. Objectives-This review focuses on understanding the basics, types, approaches, applications, advantages and disadvantages of docking. Discussion-The basics of docking involve study of the ligands and proteins. The types of docking encompass Rigid Docking, Flexible-Rigid Docking and Flexible Docking. The approaches include determination of the energy profile for the docked conformer of the ligand target and determination of the complementarity of surface

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Son, Ahrum, Woojin Kim, Jongham Park, et al. "Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics." International Journal of Molecular Sciences 25, no. 17 (2024): 9725. http://dx.doi.org/10.3390/ijms25179725.

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Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of pr

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Li, Peng, Shufang Guo, Chenghao Zhang, Mosharaf Md Parvej, and Jing Zhang. "A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding." Applied Sciences 14, no. 10 (2024): 4090. http://dx.doi.org/10.3390/app14104090.

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The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein netw

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Zhao, Jie, Xiujuan Lei, and Fang-Xiang Wu. "Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC." Complexity 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4120506.

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Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the

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Hu, Wenqian, Xu Zhang, and Juan Alvarez-Dominguez. "Widespread and Dynamic Translational Control of Red Blood Cell Development." Blood 128, no. 22 (2016): 1260. http://dx.doi.org/10.1182/blood.v128.22.1260.1260.

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Abstract Cell development requires tight yet dynamic control of protein production. Here, we use murine erythropoiesis as a model to study translational regulatory dynamics during mammalian cell differentiation. We uncover pervasive translational control of protein synthesis, including widespread alternative translation initiation and termination, stoichiometric synthesis, and dynamic use of upstream reading frames. In addition, we identify hundreds of mRNAs with dynamic translation efficiencies during erythropoiesis. Interestingly, these mRNAs have significantly enriched binding motifs of man

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Xu, Xiuhan, Yuan Zhao, Zhenbang Zhu, Wei Wen, and Xiangdong Li. "Mitofusin-Mediated Mitochondrial Fusion Inhibits Pseudorabies Virus Infection in Porcine Cells." Veterinary Sciences 12, no. 4 (2025): 368. https://doi.org/10.3390/vetsci12040368.

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Background: Mitochondria are highly dynamic organelles that undergo fusion/fission dynamics, and emerging evidence has established that mitochondrial dynamics plays a crucial regulatory role in the process of viral infection. Nevertheless, the function of mitochondria dynamics during pseudorabies (PRV) infection remains uncertain. Methods: Our investigation commenced with examining PRV-induced alterations in mitochondrial dynamics, focusing on morphological changes and the expression levels of fusion/fission proteins. We then restored mitochondrial dynamics through Mfn1 (Mitofusin 1)/Mfn2 (Mit

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35

Karaca, Ezgi, Chantal Prévost, and Sophie Sacquin-Mora. "Modeling the Dynamics of Protein–Protein Interfaces, How and Why?" Molecules 27, no. 6 (2022): 1841. http://dx.doi.org/10.3390/molecules27061841.

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Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when mode

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36

Marianayagam, Neelan J., and Sophie E. Jackson. "Native-state dynamics of the ubiquitin family: implications for function and evolution." Journal of The Royal Society Interface 2, no. 2 (2005): 47–54. http://dx.doi.org/10.1098/rsif.2004.0025.

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Protein dynamics are integral to protein function. In recent years, the use of computer simulation to understand the molecular motions of proteins has become widespread. However, there are few such studies which compare the dynamics of proteins that are structurally and functionally related. In this study, we present native-state molecular dynamic simulations of four proteins which possess a ubiquitin-like fold. Three of these proteins are thought to have evolved from a common ancestral ubiquitin-like protein and have similarities in their function. A fourth protein, which is structurally homo

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37

Ahuja, Lalima G., Phillip C. Aoto, Alexandr P. Kornev, Gianluigi Veglia, and Susan S. Taylor. "Dynamic allostery-based molecular workings of kinase:peptide complexes." Proceedings of the National Academy of Sciences 116, no. 30 (2019): 15052–61. http://dx.doi.org/10.1073/pnas.1900163116.

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A dense interplay between structure and dynamics underlies the working of proteins, especially enzymes. Protein kinases are molecular switches that are optimized for their regulation rather than catalytic turnover rates. Using long-simulations dynamic allostery analysis, this study describes an exploration of the dynamic kinase:peptide complex. We have used protein kinase A (PKA) as a model system as a generic prototype of the protein kinase superfamily of signaling enzymes. Our results explain the role of dynamic coupling of active-site residues that must work in coherence to provide for a su

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Campbell, Edward M., Mark P. Dodding, Melvyn W. Yap та ін. "TRIM5α Cytoplasmic Bodies Are Highly Dynamic Structures". Molecular Biology of the Cell 18, № 6 (2007): 2102–11. http://dx.doi.org/10.1091/mbc.e06-12-1075.

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Tripartite motif (TRIM)5α has recently been identified as a host restriction factor that has the ability to block infection by certain retroviruses in a species-dependent manner. One interesting feature of this protein is that it is localized in distinct cytoplasmic clusters designated as cytoplasmic bodies. The potential role of these cytoplasmic bodies in TRIM5α function remains to be defined. By using fluorescent fusion proteins and live cell microscopy, we studied the localization and dynamics of TRIM5α cytoplasmic bodies. This analysis reveals that cytoplasmic bodies are highly mobile, ex

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Parker, Amelia L., Wee Siang Teo, Simon Brayford та ін. "βIII-Tubulin Structural Domains Regulate Mitochondrial Network Architecture in an Isotype-Specific Manner". Cells 11, № 5 (2022): 776. http://dx.doi.org/10.3390/cells11050776.

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βIII-tubulin is a neuronal microtubule protein that is aberrantly expressed in epithelial cancers. The microtubule network is implicated in regulating the architecture and dynamics of the mitochondrial network, although the isotype-specific role for β-tubulin proteins that constitute this microtubule network remains unclear. High-resolution electron microscopy revealed that manipulation of βIII-tubulin expression levels impacts the volume and shape of mitochondria. Analysis of the structural domains of the protein identifies that the C-terminal tail of βIII-tubulin, which distinguishes this pr

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Barnoud, Jonathan, Hubert Santuz, Pierrick Craveur, et al. "PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks." PeerJ 5 (November 20, 2017): e4013. http://dx.doi.org/10.7717/peerj.4013.

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This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deforma

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Salaun, Christine, Jennifer Greaves, and Luke H. Chamberlain. "The intracellular dynamic of protein palmitoylation." Journal of Cell Biology 191, no. 7 (2010): 1229–38. http://dx.doi.org/10.1083/jcb.201008160.

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S-palmitoylation describes the reversible attachment of fatty acids (predominantly palmitate) onto cysteine residues via a labile thioester bond. This posttranslational modification impacts protein functionality by regulating membrane interactions, intracellular sorting, stability, and membrane micropatterning. Several recent findings have provided a tantalizing insight into the regulation and spatiotemporal dynamics of protein palmitoylation. In mammalian cells, the Golgi has emerged as a possible super-reaction center for the palmitoylation of peripheral membrane proteins, whereas palmitoyla

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Zheng, Lihua, Peng Liu, Qianwen Liu, Tao Wang, and Jiangli Dong. "Dynamic Protein S-Acylation in Plants." International Journal of Molecular Sciences 20, no. 3 (2019): 560. http://dx.doi.org/10.3390/ijms20030560.

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Lipid modification is an important post-translational modification. S-acylation is unique among lipid modifications, as it is reversible and has thus attracted much attention. We summarize some proteins that have been shown experimentally to be S-acylated in plants. Two of these S-acylated proteins have been matched to the S-acyl transferase. More importantly, the first protein thioesterase with de-S-acylation activity has been identified in plants. This review shows that S-acylation is important for a variety of different functions in plants and that there are many unexplored aspects of S-acy

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Jensen, R. "Proteins on the move: dynamic protein localization in prokaryotes." Trends in Cell Biology 10, no. 11 (2000): 483–88. http://dx.doi.org/10.1016/s0962-8924(00)01840-7.

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Ou-Yang, Le, Dao-Qing Dai, Xiao-Li Li, Min Wu, Xiao-Fei Zhang, and Peng Yang. "Detecting temporal protein complexes from dynamic protein-protein interaction networks." BMC Bioinformatics 15, no. 1 (2014): 335. http://dx.doi.org/10.1186/1471-2105-15-335.

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Kolbaba-Kartchner, Bethany, I. Can Kazan, Jeremy H. Mills та S. Banu Ozkan. "The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability". International Journal of Molecular Sciences 22, № 6 (2021): 2895. http://dx.doi.org/10.3390/ijms22062895.

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The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the

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von Bülow, Sören, Marc Siggel, Max Linke, and Gerhard Hummer. "Dynamic cluster formation determines viscosity and diffusion in dense protein solutions." Proceedings of the National Academy of Sciences 116, no. 20 (2019): 9843–52. http://dx.doi.org/10.1073/pnas.1817564116.

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We develop a detailed description of protein translational and rotational diffusion in concentrated solution on the basis of all-atom molecular dynamics simulations in explicit solvent. Our systems contain up to 540 fully flexible proteins with 3.6 million atoms. In concentrated protein solutions (100 mg/mL and higher), the proteins ubiquitin and lysozyme, as well as the protein domains third IgG-binding domain of protein G and villin headpiece, diffuse not as isolated particles, but as members of transient clusters between which they constantly exchange. A dynamic cluster model nearly quantit

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Garbett, Damien, and Anthony Bretscher. "PDZ interactions regulate rapid turnover of the scaffolding protein EBP50 in microvilli." Journal of Cell Biology 198, no. 2 (2012): 195–203. http://dx.doi.org/10.1083/jcb.201204008.

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Scaffolding proteins containing PDZ (postsynaptic density 95/discs large/zonula occludens-1) domains are believed to provide relatively stable linkages between components of macromolecular complexes and in some cases to bridge to the actin cytoskeleton. The microvillar scaffolding protein EBP50 (ERM-binding phosphoprotein of 50 kD), consisting of two PDZ domains and an ezrin-binding site, retains specific proteins in microvilli and is necessary for microvillar biogenesis. Our analysis of the dynamics of microvillar proteins in vivo indicated that ezrin and microvillar membrane proteins had dyn

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Henley, Matthew J., Brian M. Linhares, Brittany S. Morgan, Tomasz Cierpicki, Carol A. Fierke, and Anna K. Mapp. "Unexpected specificity within dynamic transcriptional protein–protein complexes." Proceedings of the National Academy of Sciences 117, no. 44 (2020): 27346–53. http://dx.doi.org/10.1073/pnas.2013244117.

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A key functional event in eukaryotic gene activation is the formation of dynamic protein–protein interaction networks between transcriptional activators and transcriptional coactivators. Seemingly incongruent with the tight regulation of transcription, many biochemical and biophysical studies suggest that activators use nonspecific hydrophobic and/or electrostatic interactions to bind to coactivators, with few if any specific contacts. Here a mechanistic dissection of a set of representative dynamic activator•coactivator complexes, comprised of the ETV/PEA3 family of activators and the coactiv

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Kilambi, Krishna Praneeth, Kavan Reddy, and Jeffrey J. Gray. "Protein-Protein Docking with Dynamic Residue Protonation States." PLoS Computational Biology 10, no. 12 (2014): e1004018. http://dx.doi.org/10.1371/journal.pcbi.1004018.

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Dykeman, Eric C. "Modelling ribosome kinetics and translational control on dynamic mRNA." PLOS Computational Biology 19, no. 1 (2023): e1010870. http://dx.doi.org/10.1371/journal.pcbi.1010870.

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The control of protein synthesis and the overall levels of various proteins in the cell is critical for achieving homoeostasis. Regulation of protein levels can occur at the transcriptional level, where the total number of messenger RNAs in the overall transcriptome are controlled, or at the translational level, where interactions of proteins and ribosomes with the messenger RNA determine protein translational efficiency. Although transcriptional control of mRNA levels is the most commonly used regulatory control mechanism in cells, positive-sense single-stranded RNA viruses often utilise tran

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