Готові списки джерел за темами / Protein association

Добірка наукової літератури з теми "Protein association"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Protein association"

1

Grueninger, D., N. Treiber, M. O. P. Ziegler, J. W. A. Koetter, M. S. Schulze, and G. E. Schulz. "Designed Protein-Protein Association." Science 319, no. 5860 (January 11, 2008): 206–9. http://dx.doi.org/10.1126/science.1150421.

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2

Camacho, Carlos J., and Sandor Vajda. "Protein–protein association kinetics and protein docking." Current Opinion in Structural Biology 12, no. 1 (February 2002): 36–40. http://dx.doi.org/10.1016/s0959-440x(02)00286-5.

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3

Pan, Albert C., Daniel Jacobson, Konstantin Yatsenko, Duluxan Sritharan, Thomas M. Weinreich, and David E. Shaw. "Atomic-level characterization of protein–protein association." Proceedings of the National Academy of Sciences 116, no. 10 (February 13, 2019): 4244–49. http://dx.doi.org/10.1073/pnas.1815431116.

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Despite the biological importance of protein–protein complexes, determining their structures and association mechanisms remains an outstanding challenge. Here, we report the results of atomic-level simulations in which we observed five protein–protein pairs repeatedly associate to, and dissociate from, their experimentally determined native complexes using a molecular dynamics (MD)–based sampling approach that does not make use of any prior structural information about the complexes. To study association mechanisms, we performed additional, conventional MD simulations, in which we observed numerous spontaneous association events. A shared feature of native association for these five structurally and functionally diverse protein systems was that if the proteins made contact far from the native interface, the native state was reached by dissociation and eventual reassociation near the native interface, rather than by extensive interfacial exploration while the proteins remained in contact. At the transition state (the conformational ensemble from which association to the native complex and dissociation are equally likely), the protein–protein interfaces were still highly hydrated, and no more than 20% of native contacts had formed.
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4

Giles, K. "Interactions underlying subunit association in cholinesterases." Protein Engineering Design and Selection 10, no. 6 (June 1, 1997): 677–85. http://dx.doi.org/10.1093/protein/10.6.677.

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5

Erickson, Harold P. "Co-operativity in protein-protein association." Journal of Molecular Biology 206, no. 3 (April 1989): 465–74. http://dx.doi.org/10.1016/0022-2836(89)90494-4.

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6

Lumry, R., and R. B. Gregory. "Dynamical factors in protein-protein association." Journal of Molecular Liquids 42 (October 1989): 113–44. http://dx.doi.org/10.1016/0167-7322(89)80029-7.

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7

Karplus, M., and J. Janin. "Comment on: `The entropy cost of protein association'." Protein Engineering, Design and Selection 12, no. 3 (March 1999): 185–86. http://dx.doi.org/10.1093/protein/12.3.185.

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8

Brandsdal, B. O., and A. O. Smalås. "Evaluation of protein–protein association energies by free energy perturbation calculations." Protein Engineering, Design and Selection 13, no. 4 (April 2000): 239–45. http://dx.doi.org/10.1093/protein/13.4.239.

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9

Suratanee, Apichat, and Kitiporn Plaimas. "Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins." International Journal of Molecular Sciences 21, no. 4 (February 15, 2020): 1310. http://dx.doi.org/10.3390/ijms21041310.

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Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.
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10

Zheng, W., N. P. Schafer, A. Davtyan, G. A. Papoian, and P. G. Wolynes. "Predictive energy landscapes for protein-protein association." Proceedings of the National Academy of Sciences 109, no. 47 (November 5, 2012): 19244–49. http://dx.doi.org/10.1073/pnas.1216215109.

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Дисертації з теми "Protein association"

1

Romero, Durana Miguel Alfonso. "Improving the description of protein-protein association energy." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/665466.

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Анотація:
Proteins play a crucial role in virtually every biological process taking place within our cells. Most of the times, proteins do not participate in these processes alone but forming complexes of two or more proteins. Therefore, the study of protein-protein interactions (PPIs) and complex formation has become an important field of research in the last decades due to its scientific relevance and therapeutic interest. Protein docking is one of the several computational approaches that have been applied to study protein interactions over the last years. It aims to determine the three-dimensional structure of a protein complex based on the structure of its subunits. Although the field has experienced important advances in recent years, it faces significant challenges ahead. New strategies are necessary to overcome current sampling limitations and enhance the physico-chemical description of protein-protein association, understanding its intrinsic mechanisms and identifying the most relevant residues involved, i.e., hot-spot residues. This Ph.D. thesis has focused on developing new computational tools to address some of these challenges. We have developed pyDockLite, a simplified scoring function derived from pyDock, the docking scoring function developed within our lab, which is up to 10 times faster at comparable performance. The key element in pyDockLite development is the new distance-based desolvation term, which drastically reduces the computation time required to calculate the desolvation contribution to pyDock docking energy. Based on pyDockLite, we have developed a fast rigid-body minimization algorithm, which is very efficient when the complex subunits are in their bound conformation. To model backbone flexibility we have included normal modes in the minimization algorithm. This new feature improves the results, especially for the medium-flexible and flexible cases. Most protein-protein docking protocols use scoring functions to evaluate docking poses and discriminate between good, i.e., near- native, and bad conformations. The implicit assumption is that the different energetic minima forming the docking energy landscape are represented by single docking poses which are scored individually. In this thesis, we have analyzed the concept that each energetic minima of the docking energy landscape can be formed by ensembles of docking orientations or conformations, and we have explored the consequences of scoring each minimum by such ensembles. We propose a novel ensemble-based description of the docking landscapes, integrating clustering, conformational sampling and consensus scoring, which improves docking performance. In some circumstances, we might want to have a more detailed description, at the level of residue or atoms, of the docking energy of the different states conforming the docking landscapes. We have developed a method to partition pyDock docking energy at the residue level. Interestingly, we will show how we can use this partitioned energy to identify energetically relevant residues in the binding process (hot-spots) and to estimate changes in binding affinity upon mutation to alanine, i.e., as an in-silico alanine scanning mutagenesis predictor. Regarding mutations to other residues, we have developed a new method to predict binding affinity changes upon mutation by combining MODELLER and pyDock. Results are in line with previous methods when tested on an external validation dataset. Finally, we have explored how to apply the knowledge and tools we have developed to other protein interactions such as those between proteins and RNA molecules. We present a new scoring function that combines FTDock score and pyDock electrostatics and van der Waals energy terms. This scoring function can be used to evaluate docking models of protein-RNA complexes. Our work indicates that protein-protein and protein-RNA interactions may have distinctive features that prevent the direct application of protein-protein scoring functions to protein-RNA docking studies
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2

Samuel, Jarvie John. "Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30508/.

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Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.
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3

Ahmad, Mazen [Verfasser], and Volkhard [Akademischer Betreuer] Helms. "Mechanisms of protein-protein association : atomistic molecular dynamics study of the association process / Mazen Ahmad. Betreuer: Volkhard Helms." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2012. http://d-nb.info/1052551688/34.

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4

Donnini, S. (Serena). "Computing free energies of protein-ligand association." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514285745.

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Abstract Spontaneous changes in protein systems, such as the binding of a ligand to an enzyme or receptor, are characterized by a decrease of free energy. Despite the recent developments in computing power and methodology, it remains challenging to accurately estimate free energy changes. Major issues are still concerned with the accuracy of the underlying model to describe the protein system and how well the calculation in fact emulates the behaviour of the system. This thesis is largely concerned with the quality of current free energy calculation methods as applied to protein-ligand systems. Several methodologies were employed to calculate Gibbs standard free energies of binding for a collection of protein-ligand complexes, for which experimental affinities were available. Calculations were performed using system description with different levels of accuracy and included a continuum approach, which considers the protein and the ligand at the atomic level but includes solvent as a polarizable continuum, and an all-atom approach that relies on molecular dynamics simulations. In most such applications, the effects of ionic strength are neglected. However, the severity of this approximation, in particular when calculating free energies of charged ligands, is not very clear. The issue of incorporating ionic strength in free energy calculations by means of explicit ions was investigated in greater detail and considerable attention was given to the affinities of charged peptides in the presence of explicit counter-ions. A second common approximation is concerned with the description of ligands that exhibit multiple protonation states. Because most of current methods do not model changes in the acid dissociation constants of titrating groups upon binding, protonation equilibria of such ligands are not taken into account in free energy calculations. The implications of this approximation when predicting affinities were analysed. Finally, when calculating free energies of binding, a correct description of the interactions between the protein and the ligand is of fundamental importance. However, active sites of enzymes, where strained conformations may hold a functional role, are not always accurately modelled by molecular mechanics force fields. The case of a strained planar proline in the active site of triosephosphate isomerase was investigated using an hybrid quantum mechanics/molecular mechanics method, which implies a higher level of accuracy.
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5

Doig, Andrew James. "Thermodynamics of peptide association and protein folding." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386389.

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6

Irudayam, Sheeba Jem. "Thermodynamics of Protein-Ligand Association and Hydration." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506575.

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7

Morrow, Robert Peter. "A study into human erythrocyte membrane protein association." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288406.

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8

Li, X. F. "Investigation of protein-protein interactions : multibody docking, association/dissociation kinetics and macromolecular crowding." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1302277/.

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Protein-protein interactions are central to understanding how cells carry out their wide array of functions and metabolic procedures. Conventional studies on specific protein interactions focus either on details of one-to-one binding interfaces, or on large networks that require a priori knowledge of binding strengths. Moreover, specific protein interactions, occurring within a crowded macromolecular environment, which is precisely the case for interactions in a real cell, are often under-investigated. A macromolecular simulation package, called BioSimz, has been developed to perform Langevin dynamics simulations on multiple protein-protein interactions at atomic resolution, aimed at bridging the gaps between structural, kinetic and crowding studies on protein-protein interactions. Simulations on twenty-seven experimentally determined protein-protein interactions, indicated that the use of contact frequency information of proteins forming specific encounters can guide docking algorithms towards the most likely binding regions. Further evidence from eleven benchmarked protein interactions showed that the association rate constant of a complex, kon, can be estimated, with good agreement to experimental values, based on the retention time of its specific encounter. Performing these simulations with ten types of environmental protein crowders, it suggests, from the change of kon, that macromolecular crowding improves the association kinetics of slower-binding proteins, while it damps the association kinetics of fast, electrostatics-driven protein-protein interactions. It is hypothesised, based on evidence from docking, kinetics and crowding, that the dynamics of specific protein-protein encounters is vitally important in determining their association affinity. There are multiple factors by which encounter dynamics, and subsequently the kon, can be influenced, such as anchor residues, long-range forces, and environmental steering via crowders’ electrostatics and/or volume exclusion. The capacity of emulating these conditions on a common platform not only provides a holistic view of interacting dynamics, but also offers the possibility of evaluating and engineering protein-protein interactions from aspects that have never been opened before.
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9

Huang, Wenhui. "Towards constructing disease relationship networks using genome-wide association studies." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/46326.

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Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks.
Master of Science
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10

Jaeger, Samira. "Network-based inference of protein function and disease-gene association." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16623.

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Proteininteraktionen sind entscheidend für zelluläre Funktion. Interaktionen reflektieren direkte funktionale Beziehungen zwischen Proteinen. Veränderungen in spezifischen Interaktionsmustern tragen zur Entstehung von Krankheiten bei. In dieser Arbeit werden funktionale und pathologische Aspekte von Proteininteraktionen analysiert, um Funktionen für bisher nicht charakterisierte Proteine vorherzusagen und Proteine mit Krankheitsphänotypen zu assoziieren. Verschiedene Methoden wurden in den letzten Jahren entwickelt, die die funktionalen Eigenschaften von Proteinen untersuchen. Dennoch bleibt ein wesentlicher Teil der Proteine, insbesondere menschliche, uncharakterisiert. Wir haben eine Methode zur Vorhersage von Proteinfunktionen entwickelt, die auf Proteininteraktionsnetzwerken verschiedener Spezies beruht. Dieser Ansatz analysiert funktionale Module, die über evolutionär konservierte Prozesse definiert werden. In diesen Modulen werden Proteinfunktionen gemeinsam über Orthologiebeziehungen und Interaktionspartner vorhergesagt. Die Integration verschiedener funktionaler Ähnlichkeiten ermöglicht die Vorhersage neuer Proteinfunktionen mit hoher Genauigkeit und Abdeckung. Die Aufklärung von Krankheitsmechanismen ist wichtig, um ihre Entstehung zu verstehen und diagnostische und therapeutische Ansätze zu entwickeln. Wir stellen einen Ansatz für die Identifizierung krankheitsrelevanter Genprodukte vor, der auf der Kombination von Proteininteraktionen, Proteinfunktionen und Netzwerkzentralitätsanalyse basiert. Gegeben einer Krankheit, werden krankheitsspezifische Netzwerke durch die Integration von direkt und indirekt interagierender Genprodukte und funktionalen Informationen generiert. Proteine in diesen Netzwerken werden anhand ihrer Zentralität sortiert. Das Einbeziehen indirekter Interaktionen verbessert die Identifizierung von Krankheitsgenen deutlich. Die Verwendung von vorhergesagten Proteinfunktionen verbessert das Ranking von krankheitsrelevanten Proteinen.
Protein interactions are essential to many aspects of cellular function. On the one hand, they reflect direct functional relationships. On the other hand, alterations in protein interactions perturb natural cellular processes and contribute to diseases. In this thesis we analyze both the functional and the pathological aspect of protein interactions to infer novel protein function for uncharacterized proteins and to associate yet uncharacterized proteins with disease phenotypes, respectively. Different experimental and computational approaches have been developed in the past to investigate the basic characteristics of proteins systematically. Yet, a substantial fraction of proteins remains uncharacterized, particularly in human. We present a novel approach to predict protein function from protein interaction networks of multiple species. The key to our method is to study proteins within modules defined by evolutionary conserved processes, combining comparative cross-species genomics with functional linkage in interaction networks. We show that integrating different evidence of functional similarity allows to infer novel functions with high precision and a very good coverage. Elucidating the pathological mechanisms is important for understanding the onset of diseases and for developing diagnostic and therapeutic approaches. We introduce a network-based framework for identifying disease-related gene products by combining protein interaction data and protein function with network centrality analysis. Given a disease, we compile a disease-specific network by integrating directly and indirectly linked gene products using protein interaction and functional information. Proteins in this network are ranked based on their network centrality. We demonstrate that using indirect interactions significantly improves disease gene identification. Predicted functions, in turn, enhance the ranking of disease-relevant proteins.
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Книги з теми "Protein association"

1

Membrane proteins: Folding, association, and design. New York: Humana Press, 2013.

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2

Protein-protein interactions. Hauppauge, N.Y: Nova Science Publisher's, 2010.

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3

Anna, Panchenko, and Przytycka Teresa, eds. Protein-protein interactions and networks: Identification, computer analysis, and prediction. London: Springer, 2008.

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4

Anna, Panchenko, and Przytycka Teresa, eds. Protein-protein interactions and networks: Identification, computer analysis, and prediction. London: Springer, 2008.

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5

Przytycka, Teresa, and Anna Panchenko. Protein-protein interactions and networks: Identification, computer analysis, and prediction. [New York]: Springer, 2010.

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6

Protein-protein complexes: Analysis, modeling and drug design. London: Imperial College Press, 2010.

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7

International Workshop on the CCN Family of Genes (5th 2008 Toronto, Canada). CCN proteins in health and disease: An overview of the Fifth International Workshop on the CCN Family of Genes. Edited by Perbal Annick, Takigawa Masaharu, and Perbal Bernard V. Dordrecht: Springer, 2010.

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8

Ren-Jang, Lin, ed. RNA-protein interaction protocols. 2nd ed. Totowa, N.J: Humana, 2008.

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9

Olds, James. A role for protein kinase C in associative learning. [Bethesda, Md.?: National Institute of Neurological and Communicative Disorders and Stroke, 1993.

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10

M, Bujnicki Janusz, ed. Prediction of protein structures, functions, and interactions. Chichester: John Wiley & Sons, 2008.

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Частини книг з теми "Protein association"

1

Ross, Philip D. "Thermodynamics of Protein-Protein Association." In Thermodynamic Data for Biochemistry and Biotechnology, 227–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-71114-5_8.

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2

Yon-Kahn, Jeannine, and Guy Hervé. "Protein-Ligand Association Equilibria." In Molecular and Cellular Enzymology, 37–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01228-0_3.

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3

Baron, Riccardo, Piotr Setny, and J. Andrew McCammon. "Hydrophobic Association and Volume-Confined Water Molecules." In Protein-Ligand Interactions, 145–70. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645947.ch8.

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4

Powers, Evan T., and Frank A. Ferrone. "Kinetic Models for Protein Misfolding and Association." In Protein Misfolding Diseases, 73–92. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470572702.ch4.

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Jaenicke, R. "Protein Stability, Folding and Association." In Immobilised Macromolecules: Application Potentials, 1–22. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3479-4_1.

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6

Brems, David N., Patricia L. Brown, Christopher Bryant, Ronald E. Chance, Richard D. DiMarchi, L. Kenney Green, Daniel C. Howey, et al. "Altering the Self-Association and Stability of Insulin by Amino Acid Replacement." In Protein Folding, 254–69. Washington, DC: American Chemical Society, 1993. http://dx.doi.org/10.1021/bk-1993-0526.ch019.

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7

Hamilton, W., J. E. Borgert, T. Hamelryck, and J. S. Marron. "Persistent Topology of Protein Space." In Association for Women in Mathematics Series, 223–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95519-9_10.

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8

Ufer, Guido, Peter Dörmann, and Dorothea Bartels. "Studying Lipid–Protein Interactions Using Protein–Lipid Overlay and Protein–Liposome Association Assays." In Methods in Molecular Biology, 391–99. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1362-7_22.

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9

Matthews, Jacqueline M. "Heteromeric Versus Homomeric Association of Protein Complexes." In Encyclopedia of Biophysics, 969–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-16712-6_182.

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10

Gupta, Nitin, Nitin Mangal, Kamal Tiwari, and Pabitra Mitra. "Mining Quantitative Association Rules in Protein Sequences." In Lecture Notes in Computer Science, 273–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677437_21.

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Тези доповідей конференцій з теми "Protein association"

1

Garcia, Beatriz, Ricardo Aler, Agapito Ledezma, and Araceli Sanchis. "Protein-protein functional association prediction using genetic programming." In the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389156.

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Hung, Fei-Hung, and Hung-Wen Chiu. "Protein-Protein Interaction Prediction based on Association Rules of Protein Functional Regions." In 2007 Second International Conference on Innovative Computing, Information and Control. IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.466.

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Solernou, Albert, Juan Fernandez-Recio, Jesús Clemente-Gallardo, Pierpaolo Bruscolini, Francisco Castejón, Pablo Echenique, and José Félix Sáenz-Lorenzo. "Computational Tools for Exploration of the Energy Landscape in Protein-Protein Association." In LARGE SCALE SIMULATIONS OF COMPLEX SYSTEMS, CONDENSED MATTER AND FUSION PLASMA: Proceedings of the BIFI2008 International Conference: Large Scale Simulations of Complex Systems, Condensed Matter and Fusion Plasma. AIP, 2008. http://dx.doi.org/10.1063/1.3033364.

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Dutta, Pratik, and Sriparna Saha. "Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.570.

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Mukhopadhyay, Anirban, Ujjwal Maulik, Sanghamitra Bandyopadhyay, and Roland Eils. "Mining association rules from HIV-human protein interactions." In 2010 International Conference on Systems in Medicine and Biology (ICSMB). IEEE, 2010. http://dx.doi.org/10.1109/icsmb.2010.5735401.

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Mingming Liu, Yanwei Huang, Liqing Zhang, and D. R. Bevan. "A new functional association-based protein complex prediction." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112418.

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Pandey, Gaurav, Michael Steinbach, Rohit Gupta, Tushar Garg, and Vipin Kumar. "Association analysis-based transformations for protein interaction networks." In the 13th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281251.

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Hongwei Wu, Yaming Lin, Fun Choi Chan, and R. Alba-Flores. "Module detection for bacteria based on spectral clustering of protein-protein functional association networks." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112415.

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Muller, Joachim D. "Following protein association in vivo with fluorescence fluctuation spectroscopy." In Biomedical Optics 2003, edited by Ammasi Periasamy and Peter T. C. So. SPIE, 2003. http://dx.doi.org/10.1117/12.487612.

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Mernea, Maria, Octavian Calborean, Livia Petrescu, Andrei Tita, Aurel Leca, Traian Dascalu, and Dan F. Mihailescu. "Protein association investigated by THz spectroscopy and molecular modeling." In Laser Applications in Life Sciences 2010, edited by Matti Kinnunen and Risto Myllylä. SPIE, 2010. http://dx.doi.org/10.1117/12.871159.

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Звіти організацій з теми "Protein association"

1

Loebenstein, Gad, William Dawson, and Abed Gera. Association of the IVR Gene with Virus Localization and Resistance. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7604922.bard.

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We have reported that localization of TMV in tobacco cultivars with the N gene, is associated with a 23 K protein (IVR) that inhibited replication of several plant viruses. This protein was also found in induced resistant tissue of Nicotiana glutinosa x Nicotiana debneyi. During the present grant we found that TMV production is enhanced in protoplasts and plants of local lesion responding tobacco cultivars exposed to 35oC, parallel to an almost complete suppression of the production of IVR. We also found that IVR is associated with resistance mechanisms in pepper cultivars. We succeeded to clone the IVR gene. In the first attempt we isolated a clone - "101" which had a specific insert of 372 bp (the full length gene for the IVR protein of 23 kD should be around 700 bp). However, attempts to isolate the full length gene did not give clear cut results, and we decided not to continue with this clone. The amino acid sequence of the N-terminus of IVR was determined and an antiserum was prepared against a synthetic peptide representing amino acids residues 1-20 of IVR. Using this antiserum as well as our polyclonal antiserum to IVR a new clone NC-330 was isolated using lamba-ZAP library. This NC-330 clone has an insert of about 1 kB with an open reading frame of 596 bp. This clone had 86.6% homology with the first 15 amino acids of the N-terminal part of IVR and 61.6% homology with the first 23 amino acids of IVR. In the QIA expression system and western blotting of the expressed protein, a clear band of about 21 kD was obtained with IVR antiserum. This clone was used for transformation of Samsun tobacco plants and we have presently plantlets which were rooted on medium containing kanamycin. Hybridization with this clone was also obtained with RNA from induced resistant tissue of Samsun NN but not with RNA from healthy control tissue of Samsun NN, or infected or healthy tissue of Samsun. This further strengthens the previous data that the NC 330 clone codes for IVR. In the U.S. it was shown that IVR is induced in plants containing the N' gene when infected with mutants of TMV that elicit the HR. This is a defined system in which the elicitor is known to be due to permutations of the coat protein which can vary in elicitor strength. The objective was to understand how IVR synthesis is induced after recognition of elicitor coat protein in the signal transduction pathway that leads to HR. We developed systems to manipulate induction of IVR by modifying the elicitor and are using these elicitor molecules to isolate the corresponding plant receptor molecules. A "far-western" procedure was developed that found a protein from N' plants that specifically bind to elicitor coat proteins. This protein is being purified and sequenced. This objective has not been completed and is still in progress. We have reported that localization of TMV in tobacco cultivars with the N gene, is associated with a 23 K protein (IVR) that inhibited replication of several plant viruses. This protein was also found in induced resistant tissue of Nicotiana glutinosa x Nicotiana debneyi.
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2

Epel, Bernard L., Roger N. Beachy, A. Katz, G. Kotlinzky, M. Erlanger, A. Yahalom, M. Erlanger, and J. Szecsi. Isolation and Characterization of Plasmodesmata Components by Association with Tobacco Mosaic Virus Movement Proteins Fused with the Green Fluorescent Protein from Aequorea victoria. United States Department of Agriculture, September 1999. http://dx.doi.org/10.32747/1999.7573996.bard.

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The coordination and regulation of growth and development in multicellular organisms is dependent, in part, on the controlled short and long-distance transport of signaling molecule: In plants, symplastic communication is provided by trans-wall co-axial membranous tunnels termed plasmodesmata (Pd). Plant viruses spread cell-to-cell by altering Pd. This movement scenario necessitates a targeting mechanism that delivers the virus to a Pd and a transport mechanism to move the virion or viral nucleic acid through the Pd channel. The identity of host proteins with which MP interacts, the mechanism of the targeting of the MP to the Pd and biochemical information on how Pd are alter are questions which have been dealt with during this BARD project. The research objectives of the two labs were to continue their biochemical, cellular and molecular studies of Pd composition and function by employing infectious modified clones of TMV in which MP is fused with GFP. We examined Pd composition, and studied the intra- and intercellular targeting mechanism of MP during the infection cycle. Most of the goals we set for ourselves were met. The Israeli PI and collaborators (Oparka et al., 1999) demonstrated that Pd permeability is under developmental control, that Pd in sink tissues indiscriminately traffic proteins of sizes of up to 50 kDa and that during the sink to source transition there is a substantial decrease in Pd permeability. It was shown that companion cells in source phloem tissue export proteins which traffic in phloem and which unload in sink tissue and move cell to cell. The TAU group employing MP:GFP as a fluorescence probe for optimized the procedure for Pd isolation. At least two proteins kinases found to be associated with Pd isolated from source leaves of N. benthamiana, one being a calcium dependent protein kinase. A number of proteins were microsequenced and identified. Polyclonal antibodies were generated against proteins in a purified Pd fraction. A T-7 phage display library was created and used to "biopan" for Pd genes using these antibodies. Selected isolates are being sequenced. The TAU group also examined whether the subcellular targeting of MP:GFP was dependent on processes that occurred only in the presence of the virus or whether targeting was a property indigenous to MP. Mutant non-functional movement proteins were also employed to study partial reactions. Subcellular targeting and movement were shown to be properties indigenous to MP and that these processes do not require other viral elements. The data also suggest post-translational modification of MP is required before the MP can move cell to cell. The USA group monitored the development of the infection and local movement of TMV in N. benthamiana, using viral constructs expressing GFP either fused to the MP of TMV or expressing GFP as a free protein. The fusion protein and/or the free GFP were expressed from either the movement protein subgenomic promoter or from the subgenomic promoter of the coat protein. Observations supported the hypothesis that expression from the cp sgp is regulated differently than expression from the mp sgp (Szecsi et al., 1999). Using immunocytochemistry and electron microscopy, it was determined that paired wall-appressed bodies behind the leading edge of the fluorescent ring induced by TMV-(mp)-MP:GFP contain MP:GFP and the viral replicase. These data suggest that viral spread may be a consequence of the replication process. Observation point out that expression of proteins from the mp sgp is temporary regulated, and degradation of the proteins occurs rapidly or more slowly, depending on protein stability. It is suggested that the MP contains an external degradation signal that contributes to rapid degradation of the protein even if expressed from the constitutive cp sgp. Experiments conducted to determine whether the degradation of GFP and MP:GFP was regulated at the protein or RNA level, indicated that regulation was at the protein level. RNA accumulation in infected protoplast was not always in correlation with protein accumulation, indicating that other mechanisms together with RNA production determine the final intensity and stability of the fluorescent proteins.
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Adhami, Vaqar M. Association Between Microtubule Associated Protein -2 and the EGRF Signaling in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada445117.

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Adhami, Vaqar M. Association between Microtubule Associated Protein -2 and the EGRF Signaling in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada466580.

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Zhou, Kechong, Yi Lu, Kang Liu, Yuxuan Song, Yongjiao Yang, and Xiaoqiang Liu. Association between C-reactive protein levels and prognosis in prostate cancer: A meta-analysis involving 13,555 subjects. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2020. http://dx.doi.org/10.37766/inplasy2020.6.0061.

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Gontar, I. P., O. A. Rusanova, O. I. Emelyanova, and I. A. Zborovskaya. ASSOCIATION BETWEEN NEUROLOGICAL STATUS OF RHEUMATOID ARTHRITIS PATIENTS WITH SPECIFIC ANTIBODIES TO MYELIN AND S-100 PROTEIN. Планета, 2018. http://dx.doi.org/10.18411/978-5-907109-24-7-2018-xxxv-89-95.

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7

Blaxter, Tamsin, and Tara Garnett. Primed for power: a short cultural history of protein. TABLE, November 2022. http://dx.doi.org/10.56661/ba271ef5.

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Protein has a singularly prominent place in discussions about food. It symbolises fitness, strength and masculinity, motherhood and care. It is the preferred macronutrient of affluence and education, the mark of a conscientious diet in wealthy countries and of wealth and success elsewhere. Through its association with livestock it stands for pastoral beauty and tradition. It is the high-tech food of science fiction, and in discussions of changing agricultural systems it is the pivotal nutrient around which good and bad futures revolve. There is no denying that we need protein and that engaging with how we produce and consume it is a crucial part of our response to the environmental crises. But discussions of these issues are affected by their cultural context—shaped by the power of protein. Given this, we argue that it is vital to map that cultural power and understand its origins. This paper explores the history of nutritional science and international development in the Global North with a focus on describing how protein gained its cultural meanings. Starting in the first half of the 19th century and running until the mid-1970s, it covers two previous periods when protein rose to singular prominence in food discourse: in the nutritional science of the late-19th century, and in international development in the post-war era. Many parallels emerge, both between these two eras and in comparison with the present day. We hope that this will help to illuminate where and why the symbolism and story of protein outpace the science—and so feed more nuanced dialogue about the future of food.
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Zhang, Ruizhe, and Qingya Xie. A meta-analysis of cholesteryl ester transfer protein(CETP) gene rs708272(G>A) polymorphism in association with cornoary heart disease risk. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2023. http://dx.doi.org/10.37766/inplasy2023.6.0021.

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Review question / Objective: To seek the association of the CETP rs708272 polymorphism with CHD.To figure out if the carriers of allele rs708272-A reduce or increase the risk of CHD in comparison with carriers of allele rs708272-G under allele model, dominant model and recessive model. Condition being studied: The inclusion criteria of CHD:(1)the presence of stenosis≥50% in a minimum of one main segment of coronary arteries (the right coronary artery, left circumfex, or left anterior descending arteries) by coronary angiography.(2) symptoms representing angina pectoris, electrocardiographic changes, and elevations of cardiac enzymes based on the criteria of the World Health Organization. (3) a certifed record of coronary artery bypass graft or percutaneous coronary intervention were included in the study.The exclusion criteria of CHD :patients with congenital heart disease, cardiomyopathy, and valvular disease.Controls:the same populations as the cases and specifed to be without CAD, cardiovascular and cerebrovascular diseases, and peripheral atherosclerotic arterial disease.
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Ohad, Nir, and Robert Fischer. Regulation of Fertilization-Independent Endosperm Development by Polycomb Proteins. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695869.bard.

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Arabidopsis mutants that we have isolated, encode for fertilization-independent endosperm (fie), fertilization-independent seed2 (fis2) and medea (mea) genes, act in the female gametophyte and allow endosperm to develop without fertilization when mutated. We cloned the FIE and MEA genes and showed that they encode WD and SET domain polycomb (Pc G) proteins, respectively. Homologous proteins of FIE and MEA in other organisms are known to regulate gene transcription by modulating chromatin structure. Based on our results, we proposed a model whereby both FIE and MEA interact to suppress transcription of regulatory genes. These genes are transcribed only at proper developmental stages, as in the central cell of the female gametophyte after fertilization, thus activating endosperm development. To test our model, the following questions were addressed: What is the Composition and Function of the Polycomb Complex? Molecular, biochemical, genetic and genomic approaches were offered to identify members of the complex, analyze their interactions, and understand their function. What is the Temporal and Spatial Pattern of Polycomb Proteins Accumulation? The use of transgenic plants expressing tagged FIE and MEA polypeptides as well as specific antibodies were proposed to localize the endogenous polycomb complex. How is Polycomb Protein Activity Controlled? To understand the molecular mechanism controlling the accumulation of FIE protein, transgenic plants as well as molecular approaches were proposed to determine whether FIE is regulated at the translational or posttranslational levels. The objectives of our research program have been accomplished and the results obtained exceeded our expectation. Our results reveal that fie and mea mutations cause parent-of-origin effects on seed development by distinct mechanisms (Publication 1). Moreover our data show that FIE has additional functions besides controlling the development of the female gametophyte. Using transgenic lines in which FIE was not expressed or the protein level was reduced during different developmental stages enabled us for the first time to explore FIE function during sporophyte development (Publication 2 and 3). Our results are consistent with the hypothesis that FIE, a single copy gene in the Arabidopsis genome, represses multiple developmental pathways (i.e., endosperm, embryogenesis, shot formation and flowering). Furthermore, we identified FIE target genes, including key transcription factors known to promote flowering (AG and LFY) as well as shoot and leaf formation (KNAT1) (Publication 2 and 3), thus demonstrating that in plants, as in mammals and insects, PcG proteins control expression of homeobox genes. Using the Yeast two hybrid system and pull-down assays we demonstrated that FIE protein interact with MEA via the N-terminal region (Publication 1). Moreover, CURLY LEAF protein, an additional member of the SET domain family interacts with FIE as well. The overlapping expression patterns of FIE, with ether MEA or CLF and their common mutant phenotypes, demonstrate the versatility of FIE function. FIE association with different SET domain polycomb proteins, results in differential regulation of gene expression throughout the plant life cycle (Publication 3). In vitro interaction assays we have recently performed demonstrated that FIE interacts with the cell cycle regulatory component Retinobalsoma protein (pRb) (Publication 4). These results illuminate the potential mechanism by which FIE may restrain embryo sac central cell division, at least partly, through interaction with, and suppression of pRb-regulated genes. The results of this program generated new information about the initiation of reproductive development and expanded our understanding of how PcG proteins regulate developmental programs along the plant life cycle. The tools and information obtained in this program will lead to novel strategies which will allow to mange crop plants and to increase crop production.
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Pirone, Thomas P., Benjamin Raccah, and Nor Chejanovsky. Vector Specificity in Potyvirus Transmission: Role of the Helper Component. United States Department of Agriculture, January 2003. http://dx.doi.org/10.32747/2003.7586456.bard.

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Objectives: The overall objective of this research was to gain a better understanding of how potyviruses interact with their aphid vectors. The aim was to design new approaches for prevention of potyvirus spread by aphids. The sub-objectives included: (1). Determination of which of the HCs of different potyviruses effect efficient transmission by specific aphid vectors; (2). Determine regions in the HC that play a role in their compatibility with the vector; (3). Determine the factors within the aphid stylets that modify HC activity in transmission. Background of the topic: Background to the topic: Potyviruses are typical non persistent viruses. They are retained within the vector’s stylets and rapidly lost by the vector. Some potyviruses greatly differ in their ability to be transmitted by different aphid species. The present work centered on analyzing factors that may modify the interactions between the "helper component"(HC), the virions and the aphid species involved. Major conclusions, solutions and achievements: It was established that specificity of transmission may depend on aphid species used. It was also shown that specificity may depend on the affinity between HC and virion. However, the attempts to create activechimericTEV/TuMVHCs or ZYMV/TuMVHCs to identify the regions that determine interaction with a specific vector(s), were not successful. More progress was attained in objective 3: In Kentucky, tests were conducted to ascertain retention tobacco vein mottling virus (TVMV) HC in the stylets of L. erysimicompared to that in M. persicae. Ultra-thin section of stylets of aphids that fed on either TuMVHC or TVMVHC antibodies were treated with gold-labeled goat anti-rabbit antibodies.TuMV was seen in 25% the stylets of L. erysimi when they acquired TuMVHC but not when they acquired TVMVHC. In M. persicae, TVMVHC was present in 30% of the stylets. . Transmission with TuMVHC was not affected by treatment with L. erysimi saliva whereas transmission with PVYHC (which also is not functional in L. erysimi) was consistently reduced by about half. Saliva from M. persicaehad essentially no effect on either HC. The possible role aphid cuticle proteins (which are found on the stylets surface) in the association with the potyviralHC was investigated in Israel. This was done adopting two approaches: (a) isolation of cuticular proteins from aphid cuticle; (b) screening for genes encoding cuticular proteins. In the first approach, we succeeded in extracting proteins from whole homogenized M. persicaeusing concentrated urea. The extracted protein served for preparation of anti cuticular antibodies. In overlay experiments it was found that cuticular proteins specifically bind to ZYMVHC. In addition, a cDNA library of M. persicae has been prepared. Genes encoding for cuticular proteins were ascertained using antibodies to cuticular proteins. This allowed reporting the sequence of the first cuticular gene of aphids and comparing it in six aphid species. Implications, scientific and agricultural: Achievements: (1) Proofs were provided for the role of the specificity of the aphid species to the HC of certain potyviruses; (2) aphid’s saliva was found to affects transmission efficiency; (3) cuticle protein genes were isolated for the first time from aphid species and an association of cuticle protein with the potyviralHC was discerned. Agricultural and/or economic impact of the research findings: At this stage of research, our finding do not bear an agricultural or economic impact.
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