Journal articles: 'Structural interaction'

1

JOHNSTON, RICHARD D., and GEOFFREY W. BARTON. "Structural interaction analysis." International Journal of Control 41, no. 4 (April 1985): 1005–13. http://dx.doi.org/10.1080/0020718508961179.

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2

Pooler, James. "Structural Spatial Interaction∗." Professional Geographer 45, no. 3 (August 1993): 297–305. http://dx.doi.org/10.1111/j.0033-0124.1993.00297.x.

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Gursoy, Attila, Ozlem Keskin, and Ruth Nussinov. "Topological properties of protein interaction networks from a structural perspective." Biochemical Society Transactions 36, no. 6 (November 19, 2008): 1398–403. http://dx.doi.org/10.1042/bst0361398.

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Protein–protein interactions are usually shown as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. The graph abstraction of protein interactions is crucial for understanding the global behaviour of the network. In this mini review, we summarize basic graph topological properties, such as node degree and betweenness, and their relation to essentiality and modularity of protein interactions. The classification of hub proteins into date and party hubs with distinct properties has significant implications for relating topological properties to the behaviour of the network. We emphasize that the integration of protein interface structure into interaction graph models provides a better explanation of hub proteins, and strengthens the relationship between the role of the hubs in the cell and their topological properties.

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Guven-Maiorov, Emine, Chung-Jung Tsai, and Ruth Nussinov. "Structural host-microbiota interaction networks." PLOS Computational Biology 13, no. 10 (October 12, 2017): e1005579. http://dx.doi.org/10.1371/journal.pcbi.1005579.

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Oke, S. A., and M. K. O. Ayomoh. "The hybrid structural interaction matrix." International Journal of Quality & Reliability Management 22, no. 6 (August 2005): 607–25. http://dx.doi.org/10.1108/02656710510604917.

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Anton, M., and F. Casciati. "Structural control against failure interaction." Journal of Structural Control 5, no. 1 (June 1998): 63–73. http://dx.doi.org/10.1002/stc.4300050104.

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Lee, Bong-Jin. "S2c2-1 Structure and Protein-Protein Interaction of Helicobacter Pylori Proteins(S2-c2: "Structural biology reveals macromolecular interaction",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S127. http://dx.doi.org/10.2142/biophys.46.s127_4.

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ZHU, ZHENGWEI, ANDREY TOVCHIGRECHKO, TATIANA BARONOVA, YING GAO, DOMINIQUE DOUGUET, NICHOLAS O'TOOLE, and ILYA A. VAKSER. "LARGE-SCALE STRUCTURAL MODELING OF PROTEIN COMPLEXES AT LOW RESOLUTION." Journal of Bioinformatics and Computational Biology 06, no. 04 (August 2008): 789–810. http://dx.doi.org/10.1142/s0219720008003679.

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Structural aspects of protein–protein interactions provided by large-scale, genome-wide studies are essential for the description of life processes at the molecular level. A methodology is developed that applies the protein docking approach (GRAMM), based on the knowledge of experimentally determined protein–protein structures (DOCKGROUND resource) and properties of intermolecular energy landscapes, to genome-wide systems of protein interactions. The full sequence-to-structure-of-complex modeling pipeline is implemented in the Genome Wide Docking Database (GWIDD) resource. Protein interaction data are imported to GWIDD from external datasets of experimentally determined interaction networks. Essential information is extracted and unified to form the GWIDD database. Structures of individual interacting proteins in the database are retrieved (if available) or modeled, and protein complex structures are predicted by the docking program. All protein sequence, structure, and docking information is conveniently accessible through a Web interface.

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DeBlasio, Stacy L., Juan D. Chavez, Mariko M. Alexander, John Ramsey, Jimmy K. Eng, Jaclyn Mahoney, Stewart M. Gray, James E. Bruce, and Michelle Cilia. "Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology." Journal of Virology 90, no. 4 (December 9, 2015): 1973–87. http://dx.doi.org/10.1128/jvi.01706-15.

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ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.IMPORTANCEThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

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Hakes, Luke, David L. Robertson, Stephen G. Oliver, and Simon C. Lovell. "Protein Interactions from Complexes: A Structural Perspective." Comparative and Functional Genomics 2007 (2007): 1–5. http://dx.doi.org/10.1155/2007/49356.

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By combining crystallographic information with protein-interaction data obtained through traditional experimental means, this paper determines the most appropriate method for generating protein-interaction networks that incorporate data derived from protein complexes. We propose that a combined method should be considered; in which complexes composed of five chains or less are decomposed using the matrix model, whereas the spoke model is used to derive pairwise interactions for those with six chains or more. The results presented here should improve the accuracy and relevance of studies investigating the topology of protein-interaction networks.

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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

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Nakashima, Masaaki, Hirotaka Ode, Takashi Kawamura, Shingo Kitamura, Yuriko Naganawa, Hiroaki Awazu, Shinya Tsuzuki, et al. "Structural Insights into HIV-1 Vif-APOBEC3F Interaction." Journal of Virology 90, no. 2 (November 4, 2015): 1034–47. http://dx.doi.org/10.1128/jvi.02369-15.

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ABSTRACTThe HIV-1 Vif protein inactivates the cellular antiviral cytidine deaminase APOBEC3F (A3F) in virus-infected cells by specifically targeting it for proteasomal degradation. Several studies identified Vif sequence motifs involved in A3F interaction, whereas a Vif-binding A3F interface was proposed based on our analysis of highly similar APOBEC3C (A3C). However, the structural mechanism of specific Vif-A3F recognition is still poorly understood. Here we report structural features of interaction interfaces for both HIV-1 Vif and A3F molecules. Alanine-scanning analysis of Vif revealed that six residues located within the conserved Vif F1-, F2-, and F3-box motifs are essential for both A3C and A3F degradation, and an additional four residues are uniquely required for A3F degradation. Modeling of the Vif structure on an HIV-1 Vif crystal structure revealed that three discontinuous flexible loops of Vif F1-, F2-, and F3-box motifs sterically cluster to form a flexible A3F interaction interface, which represents hydrophobic and positively charged surfaces. We found that the basic Vif interface patch (R17, E171, and R173) involved in the interactions with A3C and A3F differs. Furthermore, our crystal structure determination and extensive mutational analysis of the A3F C-terminal domain demonstrated that the A3F interface includes a unique acidic stretch (L291, A292, R293, and E324) crucial for Vif interaction, suggesting additional electrostatic complementarity to the Vif interface compared with the A3C interface. Taken together, these findings provide structural insights into the A3F-Vif interaction mechanism, which will provide an important basis for development of novel anti-HIV-1 drugs using cellular cytidine deaminases.IMPORTANCEHIV-1 Vif targets cellular antiviral APOBEC3F (A3F) enzyme for degradation. However, the details on the structural mechanism for specific A3F recognition remain unclear. This study reports structural features of interaction interfaces for both HIV-1 Vif and A3F molecules. Three discontinuous sequence motifs of Vif, F1, F2, and F3 boxes, assemble to form an A3F interaction interface. In addition, we determined a crystal structure of the wild-type A3F C-terminal domain responsible for the Vif interaction. These results demonstrated that both electrostatic and hydrophobic interactions are the key force driving Vif-A3F binding and that the Vif-A3F interfaces are larger than the Vif-A3C interfaces. These findings will allow us to determine the configurations of the Vif-A3F complex and to construct a structural model of the complex, which will provide an important basis for inhibitor development.

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Drobakha, Hr, I. Neklonskyi, A. Kateshchenok, V. Sobyna, D. Taraduda, L. Borysova, and I. Lysachenko. "Structural and functional simulation of interaction in the field of aviation safety by using matrices." Archives of Materials Science and Engineering 2, no. 95 (February 1, 2019): 74–84. http://dx.doi.org/10.5604/01.3001.0013.1734.

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Purpose: The conducted research was aimed at constructing a structural and functional model for the interaction of bodies providing aviation safety during crisis management. Design/methodology/approach: The methods of mathematical simulation and the graph theory, the methods comparison and formalization have been applied to study the process of interaction between the bodies assuring aviation safety. Using methods of the linear algebra allowed constructing a mathematical model for the functional structure of the interaction process that contains description of this process by the main methods of interaction. Findings: It has been proved that the interaction process has a certain functional properties that reflect the functional relations between the modes of violator actions, the modes of using the response forces and the modes of interaction. A structural and functional model of interaction in semantic, algebraic forms and in the form of graphs has been created. using typical operations with incidence matrices, the possibility of obtaining the physical interpretation of the simulation results within the introduced algebra of functional structure models has been justified. Research limitations/implications: Discusses interactions between the bodies that assure aviation safety and at the same time, the possibility of a crisis situation is taken into account. Practical implications: The developed models allow reflecting the current state of the functional system and the elements of the process of interaction rather completely. It makes a structural and functional analysis of interaction possible and allows defining the priority directions of its organization, simulating the options and methods of interaction in solving relevant tasks by the bodies that assure aviation safety. Originality/value: That allowed not only describing the formal relations between the methods of interaction and interacting units, between the interacting units and the modes of violator actions, but also considering the influence of the interaction process on the current state of the functional system.

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Chory, M. A., M. D. Roesler, V. A. Spector, E. Bayo, H. Flashner, and F. Jabbari. "Control Structural Interaction Testbed: A Model for University Industry Interaction." IFAC Proceedings Volumes 25, no. 12 (June 1991): 97–102. http://dx.doi.org/10.1016/s1474-6670(17)50096-8.

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Zhang, Heng, Ke Liu, Natsuko Izumi, Haiming Huang, Deqiang Ding, Zuyao Ni, Sachdev S. Sidhu, Chen Chen, Yukihide Tomari, and Jinrong Min. "Structural basis for arginine methylation-independent recognition of PIWIL1 by TDRD2." Proceedings of the National Academy of Sciences 114, no. 47 (November 8, 2017): 12483–88. http://dx.doi.org/10.1073/pnas.1711486114.

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The P-element–induced wimpy testis (PIWI)-interacting RNA (piRNA) pathway plays a central role in transposon silencing and genome protection in the animal germline. A family of Tudor domain proteins regulates the piRNA pathway through direct Tudor domain–PIWI interactions. Tudor domains are known to fulfill this function by binding to methylated PIWI proteins in an arginine methylation-dependent manner. Here, we report a mechanism of methylation-independent Tudor domain–PIWI interaction. Unlike most other Tudor domains, the extended Tudor domain of mammalian Tudor domain-containing protein 2 (TDRD2) preferentially recognizes an unmethylated arginine-rich sequence from PIWI-like protein 1 (PIWIL1). Structural studies reveal an unexpected Tudor domain-binding mode for the PIWIL1 sequence in which the interface of Tudor and staphylococcal nuclease domains is primarily responsible for PIWIL1 peptide recognition. Mutations disrupting the TDRD2–PIWIL1 interaction compromise piRNA maturation via 3′-end trimming in vitro. Our work presented here reveals the molecular divergence of the interactions between different Tudor domain proteins and PIWI proteins.

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Djinovic-Carugo, Kristina, and Oliviero Carugo. "Structural Portrait of Filamin Interaction Mechanisms." Current Protein & Peptide Science 11, no. 7 (November 1, 2010): 639–50. http://dx.doi.org/10.2174/138920310794109111.

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Jackson, Verity A., Daniel del Toro, Maria Carrasquero, Pietro Roversi, Karl Harlos, Rüdiger Klein, and Elena Seiradake. "Structural Basis of Latrophilin-FLRT Interaction." Structure 23, no. 4 (April 2015): 774–81. http://dx.doi.org/10.1016/j.str.2015.01.013.

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Yousef, Mohammad S., and Brian W. Matthews. "Structural Basis of Prospero-DNA Interaction." Structure 13, no. 4 (April 2005): 601–7. http://dx.doi.org/10.1016/j.str.2005.01.023.

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STALKER, RUTH, and IAN F. C. SMITH. "Structural monitoring using engineer–computer interaction." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 3 (June 2002): 203–18. http://dx.doi.org/10.1017/s0890060402163062.

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Engineer–computer interaction (ECI) is a new subdomain of human–computer interaction that is specifically tailored to engineers' needs. ECI uses an information classification schema, provides a modular approach to task decomposition, and integrates standard engineering characteristics and working procedures into software. A software tool kit that interprets monitoring data taken from bridges was developed according to ECI guidelines. This tool kit was given to engineers for testing and evaluation. An empirical evaluation using questionnaires was performed. The results show that this ECI software corresponds to engineers' needs and the ECI approach has potential applications to other engineering tasks.

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Chi, Seung-Wook, Si-Hyung Lee, Do-Hyoung Kim, Min-Jung Ahn, Jae-Sung Kim, Jin-Young Woo, Takuya Torizawa, Masatsune Kainosho, and Kyou-Hoon Han. "Structural Details on mdm2-p53 Interaction." Journal of Biological Chemistry 280, no. 46 (September 13, 2005): 38795–802. http://dx.doi.org/10.1074/jbc.m508578200.

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Herbst, Sabine, Noa Lipstein, Olaf Jahn, and Andrea Sinz. "Structural insights into calmodulin/Munc13 interaction." Biological Chemistry 395, no. 7-8 (July 1, 2014): 763–68. http://dx.doi.org/10.1515/hsz-2014-0134.

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Abstract Munc13 proteins are essential presynaptic regulators that mediate synaptic vesicle priming and play a role in the regulation of neuronal short-term synaptic plasticity. All four Munc13 isoforms share a common domain structure, including a calmodulin (CaM) binding site in their otherwise divergent N-termini. Here, we summarize recent results on the investigation of the CaM/Munc13 interaction. By combining chemical cross-linking, photoaffinity labeling, and mass spectrometry, we showed that all neuronal Munc13 isoforms exhibit similar CaM binding modes. Moreover, we demonstrated that the 1-5-8-26 CaM binding motif discovered in Munc13-1 cannot be induced in the classical CaM target skMLCK, indicating unique features of the Munc13 CaM binding motif.

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Liu, Shitao. "Inverse problem for structural acoustic interaction." Nonlinear Analysis: Theory, Methods & Applications 74, no. 7 (April 2011): 2647–62. http://dx.doi.org/10.1016/j.na.2010.12.020.

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Hadid, Mohamed, and Mounir K. Berrah. "Structural response for stochastic kinematic interaction." Earthquake Engineering & Structural Dynamics 30, no. 1 (2000): 97–114. http://dx.doi.org/10.1002/1096-9845(200101)30:1<97::aid-eqe998>3.0.co;2-t.

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Kovačič, Lidija, Nejc Paulič, Adrijana Leonardi, Vesna Hodnik, Gregor Anderluh, Zdravko Podlesek, Darja Žgur-Bertok, Igor Križaj, and Matej Butala. "Structural insight into LexA–RecA* interaction." Nucleic Acids Research 41, no. 21 (August 21, 2013): 9901–10. http://dx.doi.org/10.1093/nar/gkt744.

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Duncan, Starkey. "A Structural–Interactionalist Approach to Interaction." Contemporary Psychology: A Journal of Reviews 37, no. 1 (January 1992): 30–31. http://dx.doi.org/10.1037/031779.

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Hirai, M., T. Takizawa, S. Yabuki, Y. Nakata, S. Arai, and M. Furusaka. "Structural study of protein-ganglioside interaction." Progress in Colloid & Polymer Science 106, no. 1 (December 1997): 237–41. http://dx.doi.org/10.1007/bf01189528.

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Martin, Juliette, Leslie Regad, Hélène Lecornet, and Anne-Claude Camproux. "Structural deformation upon protein-protein interaction: A structural alphabet approach." BMC Structural Biology 8, no. 1 (2008): 12. http://dx.doi.org/10.1186/1472-6807-8-12.

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Roy, Paushali, and Abhijit Datta. "DCL and Associated Proteins of Arabidopsis thaliana - An Interaction Study." International Letters of Natural Sciences 61 (January 2017): 85–94. http://dx.doi.org/10.18052/www.scipress.com/ilns.61.85.

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During RNA interference in plants, Dicer-like/DCL proteins process longer double-stranded RNA (dsRNA) precursors into small RNA molecules. In Arabidopsis thaliana there are four DCLs (DCL1, DCL2, DCL3, and DCL4) that interact with various associated proteins to carry out this processing. The lack of complete structural-functional information and characterization of DCLs and their associated proteins leads to this study where we have generated the structures by modelling, analysed the structures and studied the interactions of Arabidopsisthaliana DCLs with their associated proteins with the homology-derived models to screen the interacting residues. Structural analyses indicate existence of significant conserved domains that may play imperative roles during protein-protein interactions. The interaction study shows some key domain-domain (including multi-domains and inter-residue interactions) interfaces and specific residue biases (like arginine and leucine) that may help in augmenting the protein expression level during stress responses. Results point towards plausible stable associations to carry out RNA processing in a synchronised pattern by elucidating the structural properties and protein-protein interactions of DCLs that may hold significance for RNAi researchers.

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Khakzad, Hamed, Lotta Happonen, Yasaman Karami, Sounak Chowdhury, Gizem Ertürk Bergdahl, Michael Nilges, Guy Tran Van Nhieu, Johan Malmström, and Lars Malmström. "Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology." PLOS Computational Biology 17, no. 1 (January 7, 2021): e1008169. http://dx.doi.org/10.1371/journal.pcbi.1008169.

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Streptococcus pyogenes (Group A streptococcus; GAS) is an important human pathogen responsible for mild to severe, life-threatening infections. GAS expresses a wide range of virulence factors, including the M family proteins. The M proteins allow the bacteria to evade parts of the human immune defenses by triggering the formation of a dense coat of plasma proteins surrounding the bacteria, including IgGs. However, the molecular level details of the M1-IgG interaction have remained unclear. Here, we characterized the structure and dynamics of this interaction interface in human plasma on the surface of live bacteria using integrative structural biology, combining cross-linking mass spectrometry and molecular dynamics (MD) simulations. We show that the primary interaction is formed between the S-domain of M1 and the conserved IgG Fc-domain. In addition, we show evidence for a so far uncharacterized interaction between the A-domain and the IgG Fc-domain. Both these interactions mimic the protein G-IgG interface of group C and G streptococcus. These findings underline a conserved scavenging mechanism used by GAS surface proteins that block the IgG-receptor (FcγR) to inhibit phagocytic killing. We additionally show that we can capture Fab-bound IgGs in a complex background and identify XLs between the constant region of the Fab-domain and certain regions of the M1 protein engaged in the Fab-mediated binding. Our results elucidate the M1-IgG interaction network involved in inhibition of phagocytosis and reveal important M1 peptides that can be further investigated as future vaccine targets.

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Moon, F. C., and E. H. Dowell. "Structural Dynamics." Applied Mechanics Reviews 38, no. 10 (October 1, 1985): 1287–89. http://dx.doi.org/10.1115/1.3143694.

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While much of the linear theory of structural dynamics has been codified in numerous computer software, important problems remain such as inverse methods (modal synthesis or system identification) and optimization problems. Nonlinear problems, however, are a fertile ground for new research, especially those involving large deformations (e.g., crash simulation) and material nonlinearities. Structure interaction problems will continue to be a fruitful area of research including fluid-structure dynamics and interaction with acoustic noise, thermal fields, soils, and electromagnetic forces. For example, new knowledge about unsteady flows around bluff bodies is needed to make significant progress with dynamic interaction problems with bridge and building structures in unsteady winds. A new field which shows great promise for application is the theory of feedback control of flexible structures. Advances in this area could pay off in near-space engineering and robotics. The training of new researchers with backgrounds in both structural dynamics and control theory and experience is a high priority for the control-structure field, however.

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Nicoludis, John M., Anna G. Green, Sanket Walujkar, Elizabeth J. May, Marcos Sotomayor, Debora S. Marks, and Rachelle Gaudet. "Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis." Proceedings of the National Academy of Sciences 116, no. 36 (August 20, 2019): 17825–30. http://dx.doi.org/10.1073/pnas.1821063116.

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Clustered protocadherins, a large family of paralogous proteins that play important roles in neuronal development, provide an important case study of interaction specificity in a large eukaryotic protein family. A mammalian genome has more than 50 clustered protocadherin isoforms, which have remarkable homophilic specificity for interactions between cellular surfaces. A large antiparallel dimer interface formed by the first 4 extracellular cadherin (EC) domains controls this interaction. To understand how specificity is achieved between the numerous paralogs, we used a combination of structural and computational approaches. Molecular dynamics simulations revealed that individual EC interactions are weak and undergo binding and unbinding events, but together they form a stable complex through polyvalency. Strongly evolutionarily coupled residue pairs interacted more frequently in our simulations, suggesting that sequence coevolution can inform the frequency of interaction and biochemical nature of a residue interaction. With these simulations and sequence coevolution, we generated a statistical model of interaction energy for the clustered protocadherin family that measures the contributions of all amino acid pairs at the interface. Our interaction energy model assesses specificity for all possible pairs of isoforms, recapitulating known pairings and predicting the effects of experimental changes in isoform specificity that are consistent with literature results. Our results show that sequence coevolution can be used to understand specificity determinants in a protein family and prioritize interface amino acid substitutions to reprogram specific protein–protein interactions.

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Virtanen, Valtteri, Susanna Räikkönen, Elina Puljula, and Maarit Karonen. "Ellagitannin–Lipid Interaction by HR-MAS NMR Spectroscopy." Molecules 26, no. 2 (January 12, 2021): 373. http://dx.doi.org/10.3390/molecules26020373.

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Ellagitannins have antimicrobial activity, which might be related to their interactions with membrane lipids. We studied the interactions of 12 different ellagitannins and pentagalloylglucose with a lipid extract of Escherichia coli by high-resolution magic angle spinning NMR spectroscopy. The nuclear Overhauser effect was utilized to measure the cross relaxation rates between ellagitannin and lipid protons. The shifting of lipid signals in 1H NMR spectra of ellagitannin–lipid mixture due to ring current effect was also observed. The ellagitannins that showed interaction with lipids had clear structural similarities. All ellagitannins that had interactions with lipids had glucopyranose cores. In addition to the central polyol, the most important structural feature affecting the interaction seemed to be the structural flexibility of the ellagitannin. Even dimeric and trimeric ellagitannins could penetrate to the lipid bilayers if their structures were flexible with free galloyl and hexahydroxydiphenoyl groups.

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Dao, Phung, Pavel Trofimovich, and Sara Kennedy. "Structural alignment in L2 task-based interaction." ITL - International Journal of Applied Linguistics 169, no. 2 (November 13, 2018): 293–320. http://dx.doi.org/10.1075/itl.17021.dao.

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Abstract This study investigated L2 structural alignment, the tendency for interlocutors to re-use a syntactic structure present in recent discourse, focusing on two information-gap interactive tasks. Thirty-four university students from diverse language backgrounds, recruited from different academic programs at a Canadian English-medium university, carried out the two information-gap interactive tasks in dyads. Interaction data were transcribed and coded for instances of structural alignment and the alignment’s characteristics in terms of structure type and accuracy. Results indicated that structural alignment occurred in L2 task-based interaction generated by both tasks. This structural repetition was linked to an improved accuracy of subsequent language production. Furthermore, the two tasks were associated with different structures that were converged on, and with varying degrees of structural alignment. These findings are discussed in terms of effects of task features on structural alignment, and the role of structural alignment in subsequent language production.

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Romanchenko, Alexander Mikhailovitch. "Generalized Structural Metamodel of Information Interaction Protocol." SPIIRAS Proceedings 1, no. 38 (February 24, 2015): 58. http://dx.doi.org/10.15622/sp.38.4.

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Dong, Xiao Li, Jun Hong Hao, Yan Zhen Wang, and Rui Hua Wang. "The Research on Soil-Structural Dynamic Interaction." Applied Mechanics and Materials 90-93 (September 2011): 2292–96. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2292.

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This paper will introduce the basic methods of soil-structure dynamic interaction at home and abroad, and analysis the advantages and disadvantages of these methods. Finally, the paper will put forward opinions on the research tendency of soil-structure dynamic interaction.

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Kelker, Matthew S., Barbara Dancheck, Tingting Ju, Rene P. Kessler, Jebecka Hudak, Angus C. Nairn, and Wolfgang Peti. "Structural Basis for Spinophilin−Neurabin Receptor Interaction†." Biochemistry 46, no. 9 (March 2007): 2333–44. http://dx.doi.org/10.1021/bi602341c.

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Ko, Kil-Wan, Jeong-Gon Ha, and Dong-Soo Kim. "Structural inertial interaction effects on foundation behavior." Soil Dynamics and Earthquake Engineering 136 (September 2020): 106238. http://dx.doi.org/10.1016/j.soildyn.2020.106238.

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Vereyken, Ingrid J., J. Albert van Kuik, Toon H. Evers, Pieter J. Rijken, and Ben de Kruijff. "Structural Requirements of the Fructan-Lipid Interaction." Biophysical Journal 84, no. 5 (May 2003): 3147–54. http://dx.doi.org/10.1016/s0006-3495(03)70039-3.

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Hausmann, Chris, Amy Jonason, and Erika Summers-Effler. "Interaction Ritual Theory and Structural Symbolic Interactionism." Symbolic Interaction 34, no. 3 (August 2011): 319–29. http://dx.doi.org/10.1525/si.2011.34.3.319.

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Samoteikin, V. V. "Interaction Between Structural Elements in Binary Glasses." Glass and Ceramics 61, no. 3/4 (March 2004): 114–16. http://dx.doi.org/10.1023/b:glac.0000034062.22966.a5.

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Kooistra, Albert J., Georgi K. Kanev, Oscar P. J. van Linden, Rob Leurs, Iwan J. P. de Esch, and Chris de Graaf. "KLIFS: a structural kinase-ligand interaction database." Nucleic Acids Research 44, no. D1 (October 22, 2015): D365—D371. http://dx.doi.org/10.1093/nar/gkv1082.

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Pike, A. C. W., A. M. Brzozowski, J. Walton, and M. Carlquist. "Structural basis of oestrogen receptor-coactivator interaction." Biochemical Society Transactions 27, no. 3 (June 1, 1999): A95. http://dx.doi.org/10.1042/bst027a095c.

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Pinheiro, Anderson S., Joseph A. Marsh, Julie D. Forman-Kay, and Wolfgang Peti. "Structural Signature of the MYPT1−PP1 Interaction." Journal of the American Chemical Society 133, no. 1 (January 12, 2011): 73–80. http://dx.doi.org/10.1021/ja107810r.

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Le Tallec, P., and J. Mouro. "Fluid structure interaction with large structural displacements." Computer Methods in Applied Mechanics and Engineering 190, no. 24-25 (March 2001): 3039–67. http://dx.doi.org/10.1016/s0045-7825(00)00381-9.

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Kashiwagi, Kazuhiro, Takuhiro Ito, and Shigeyuki Yokoyama. "Structural insight into the eIF2-eIF2B interaction." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1390. http://dx.doi.org/10.1107/s2053273314086094.

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Abstract:

eIF2B (eukaryotic initiation factor 2B) is a key regulator of translation initiation. It catalyzes guanine nucleotide exchange on eIF2, which delivers the methionylated initiator tRNA to the 40S ribosomal subunit. This exchange reaction is inhibited by the stress-induced phosphorylation of the eIF2 alpha subunit, which leads to global repression of cellular protein synthesis. eIF2B is composed of five subunits. The catalytic gamma/epsilon subcomplex is responsible for nucleotide exchange, while the regulatory alpha/beta/delta subcomplex discriminates the phosphorylation status of the eIF2 alpha subunit. We established a bacterial expression system for eIF2B, and determined its crystal structure at 3.2 Å resolution. The crystal structure revealed that eIF2B is a decamer containing two molecules of each subunit. The hexameric regulatory subcomplex is formed by the trimerization of one alpha-alpha homodimer unit and two beta-delta heterodimer units, and two catalytic subcomplexes are individually connected to the regulatory subcomplex through two beta-delta heterodimer units. Photo-cross-linking analyses showed that the N-terminal domain of the eIF2 alpha subunit, which bears the phosphorylation site, is recognized by a composite surface formed by the eIF2B alpha, beta, and delta subunits. Based on these results, we report structural insights into the interaction between eIF2 and eIF2B.

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Wong, H. L., and J. E. Luco. "Structural Control Including Soil‐Structure Interaction Effects." Journal of Engineering Mechanics 117, no. 10 (October 1991): 2237–50. http://dx.doi.org/10.1061/(asce)0733-9399(1991)117:10(2237).

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Smethurst, Peter A., David J. Onley, Gavin E. Jarvis, Marie N. O'Connor, C. Graham Knight, Andrew B. Herr, Willem H. Ouwehand, and Richard W. Farndale. "Structural Basis for the Platelet-Collagen Interaction." Journal of Biological Chemistry 282, no. 2 (November 2, 2006): 1296–304. http://dx.doi.org/10.1074/jbc.m606479200.

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Bugaeva, E. Y., S. Surkov, A. V. Golovin, L. G. Ofverstedt, U. Skoglund, L. A. Isaksson, A. A. Bogdanov, O. V. Shpanchenko, and O. A. Dontsova. "Structural features of the tmRNA-ribosome interaction." RNA 15, no. 12 (October 27, 2009): 2312–20. http://dx.doi.org/10.1261/rna.1584209.

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Kiel, Christina, Pedro Beltrao, and Luis Serrano. "Analyzing Protein Interaction Networks Using Structural Information." Annual Review of Biochemistry 77, no. 1 (June 2008): 415–41. http://dx.doi.org/10.1146/annurev.biochem.77.062706.133317.

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Beavil, A. J., R. L. Beavil, C. M. W. Chan, J. P. D. Cook, H. J. Gould, A. J. Henry, R. J. Owens, J. Shi, B. J. Sutton, and R. J. Young. "Structural basis of the IgE-FcɛRI interaction." Biochemical Society Transactions 21, no. 4 (November 1, 1993): 968–72. http://dx.doi.org/10.1042/bst0210968.

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Journal articles on the topic 'Structural interaction'

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