Relevant bibliographies by topics / Computational analyses protein

Academic literature on the topic 'Computational analyses protein'

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Published: 6 September 2023

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Journal articles on the topic "Computational analyses protein"

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Chen, Yu, and Dong Xu. "Computational Analyses of High-Throughput Protein-Protein Interaction Data." Current Protein & Peptide Science 4, no. 3 (June 1, 2003): 159–80. http://dx.doi.org/10.2174/1389203033487225.

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Gruber, Jan, Alexander Zawaira, Rhodri Saunders, C. Paul Barrett, and Martin E. M. Noble. "Computational analyses of the surface properties of protein–protein interfaces." Acta Crystallographica Section D Biological Crystallography 63, no. 1 (December 13, 2006): 50–57. http://dx.doi.org/10.1107/s0907444906046762.

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Gao, Xinjiao, Changjiang Jin, Yu Xue, and Xuebiao Yao. "Computational Analyses of TBC Protein Family in Eukaryotes." Protein & Peptide Letters 15, no. 5 (June 1, 2008): 505–9. http://dx.doi.org/10.2174/092986608784567483.

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Sarkar, Anita, Sonu Kumar, Abhinav Grover, and Durai Sundar. "Protein Aggregation in Neurodegenerative Diseases: Insights from Computational Analyses." Current Bioinformatics 7, no. 1 (March 1, 2012): 87–95. http://dx.doi.org/10.2174/157489312799304495.

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Gumerov, Vadim M., and Igor B. Zhulin. "TREND: a platform for exploring protein function in prokaryotes based on phylogenetic, domain architecture and gene neighborhood analyses." Nucleic Acids Research 48, W1 (April 13, 2020): W72—W76. http://dx.doi.org/10.1093/nar/gkaa243.

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Abstract Key steps in a computational study of protein function involve analysis of (i) relationships between homologous proteins, (ii) protein domain architecture and (iii) gene neighborhoods the corresponding proteins are encoded in. Each of these steps requires a separate computational task and sets of tools. Currently in order to relate protein features and gene neighborhoods information to phylogeny, researchers need to prepare all the necessary data and combine them by hand, which is time-consuming and error-prone. Here, we present a new platform, TREND (tree-based exploration of neighborhoods and domains), which can perform all the necessary steps in automated fashion and put the derived information into phylogenomic context, thus making evolutionary based protein function analysis more efficient. A rich set of adjustable components allows a user to run the computational steps specific to his task. TREND is freely available at http://trend.zhulinlab.org.
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MORIMOTO, Yasumasa, Takashi NAKAGAWA, and Masaki KOJIMA. "Computational Analyses of Protein Structures by Solution X-ray Scattering." Seibutsu Butsuri 51, no. 2 (2011): 088–91. http://dx.doi.org/10.2142/biophys.51.088.

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Mahmood, Niaz, and Nahid Tamanna. "Analyses of Physcomitrella patens Ankyrin Repeat Proteins by Computational Approach." Molecular Biology International 2016 (June 27, 2016): 1–8. http://dx.doi.org/10.1155/2016/9156735.

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Ankyrin (ANK) repeat containing proteins are evolutionary conserved and have functions in crucial cellular processes like cell cycle regulation and signal transduction. In this study, through an entirely in silico approach using the first release of the moss genome annotation, we found that at least 54 ANK proteins are present in P. patens. Based on their differential domain composition, the identified ANK proteins were classified into nine subfamilies. Comparative analysis of the different subfamilies of ANK proteins revealed that P. patens contains almost all the known subgroups of ANK proteins found in the other angiosperm species except for the ones having the TPR domain. Phylogenetic analysis using full length protein sequences supported the subfamily classification where the members of the same subfamily almost always clustered together. Synonymous divergence (dS) and nonsynonymous divergence (dN) ratios showed positive selection for the ANK genes of P. patens which probably helped them to attain significant functional diversity during the course of evolution. Taken together, the data provided here can provide useful insights for future functional studies of the proteins from this superfamily as well as comparative studies of ANK proteins.
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Santiago, Luis, and Ravinder Abrol. "Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods." International Journal of Molecular Sciences 20, no. 21 (October 24, 2019): 5290. http://dx.doi.org/10.3390/ijms20215290.

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The neurotransmitter molecule acetylcholine is capable of activating five muscarinic acetylcholine receptors, M1 through M5, which belong to the superfamily of G-protein-coupled receptors (GPCRs). These five receptors share high sequence and structure homology; however, the M1, M3, and M5 receptor subtypes signal preferentially through the Gαq/11 subset of G proteins, whereas the M2 and M4 receptor subtypes signal through the Gαi/o subset of G proteins, resulting in very different intracellular signaling cascades and physiological effects. The structural basis for this innate ability of the M1/M3/M5 set of receptors and the highly homologous M2/M4 set of receptors to couple to different G proteins is poorly understood. In this study, we used molecular dynamics (MD) simulations coupled with thermodynamic analyses of M1 and M2 receptors coupled to both Gαi and Gαq to understand the structural basis of the M1 receptor’s preference for the Gαq protein and the M2 receptor’s preference for the Gαi protein. The MD studies showed that the M1 and M2 receptors can couple to both Gα proteins such that the M1 receptor engages with the two Gα proteins in slightly different orientations and the M2 receptor engages with the two Gα proteins in the same orientation. Thermodynamic studies of the free energy of binding of the receptors to the Gα proteins showed that the M1 and M2 receptors bind more strongly to their cognate Gα proteins compared to their non-cognate ones, which is in line with previous experimental studies on the M3 receptor. A detailed analysis of receptor–G protein interactions showed some cognate-complex-specific interactions for the M2:Gαi complex; however, G protein selectivity determinants are spread over a large overlapping subset of residues. Conserved interaction between transmembrane helices 5 and 6 far away from the G-protein-binding receptor interface was found only in the two cognate complexes and not in the non-cognate complexes. An analysis of residues implicated previously in G protein selectivity, in light of the cognate and non-cognate structures, shaded a more nuanced role of those residues in affecting G protein selectivity. The simulation of both cognate and non-cognate receptor–G protein complexes fills a structural gap due to difficulties in determining non-cognate complex structures and provides an enhanced framework to probe the mechanisms of G protein selectivity exhibited by most GPCRs.
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Carija, Pinheiro, Iglesias, and Ventura. "Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation." Cells 8, no. 8 (August 8, 2019): 856. http://dx.doi.org/10.3390/cells8080856.

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The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.
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Bottini, Silvia, David Pratella, Valerie Grandjean, Emanuela Repetto, and Michele Trabucchi. "Recent computational developments on CLIP-seq data analysis and microRNA targeting implications." Briefings in Bioinformatics 19, no. 6 (June 12, 2017): 1290–301. http://dx.doi.org/10.1093/bib/bbx063.

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AbstractCross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.
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Dissertations / Theses on the topic "Computational analyses protein"

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Croft, Edward. "Computational analyses of protein-ligand interactions." Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265562.

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Haider, Kamran. "Computational analyses of protein-ligand interactions." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1242/.

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Protein-ligand interactions have a central role in all processes in living systems. A comprehensive understanding of protein interactions with small molecules is of great interest as it provides opportunities for understanding protein function and therapeutic intervention. The major aims of this thesis were to characterise protein-ligand interactions from databases of crystal structures and to apply molecular modelling techniques for accurate prediction of binding modes of molecular fragments in protein binding sites. The first aspect of the project was the analysis of hydrogen bond donors and acceptors in 187 protein-ligand complexes of resolution 2.5Å or better. The results showed that an extremely small fraction of them were not explicitly hydrogen bonded, with the hydrogen bond criterion of donor-acceptor distance ≤ 3.5 Å and H-bond angle of ≥ 90°. It was also noticed that a vast majority of such cases were explicable on the basis of weak interactions and weak donor/acceptor strength. The results were consistent with reported observations for buried protein regions. In a series of docking calculations, the fraction of lost hydrogen bonds was evaluated as a discriminator of good versus bad docking poses. Docking and scoring with a standard program, rDock, did not create incorrect poses with missing hydrogen bonds to an extent that would make lost hydrogen bonds a strong discriminator. The second aspect of the research is related to weak (CH-π and XH-π, X=N,O,S) interactions. In a survey of IsoStar, a database of protein-ligand interactions, subtle differences were noticed in geometric parameters of π interactions involving different types of ligand aromatic rings with strong and weak donor groups in binding sites. The results supported the hypothesis that energetically favourable interaction patterns are more frequent when there are electron-donating substituents attached to the aromatic ring. Finally, the applicability of a modelling technique, multiple copy simultaneous search, in terms of predicting energetically favourable poses of solvents and fragments in target binding sites, was explored in detail. Several factors such as re-scoring with a better scoring function, use of multiple receptor structures and good quality prediction of water binding sites led to a robust protocol for high quality predictions of fragment binding in test datasets.
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Haberman, N. "Insights into protein-RNA complexes from computational analyses of iCLIP experiments." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1568450/.

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RNA-binding proteins (RBPs) are the primary regulators of all aspects of post-transcriptional gene regulation. In order to understand how RBPs perform their function, it is important to identify their binding sites. Recently, new techniques have been developed to employ high-throughput sequencing to study protein-RNA interactions in vivo, including the individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP). iCLIP identifies sites of protein-RNA crosslinking with nucleotide resolution in a transcriptome-wide manner. It is composed of over 60 steps, which can be modified, but it is not clear how variations in the method affect the assignment of RNA binding sites. This is even more pertinent given that several variants of iCLIP have been developed. A central question of my research is how to correctly assign binding sites to RBPs using the data produced by iCLIP and similar techniques. I first focused on the technical analyses and solutions for the iCLIP method. I examined cDNA deletions and crosslink-associated motifs to show that the starts of cDNAs are appropriate to assign the crosslink sites in all variants of CLIP, including iCLIP, eCLIP and irCLIP. I also showed that the non-coinciding cDNA-starts are caused by technical conditions in the iCLIP protocol that may lead to sequence constraints at cDNA-ends in the final cDNA library. I also demonstrated the importance of fully optimizing the RNase and purification conditions in iCLIP to avoid these cDNA-end constraints. Next, I developed CLIPo, a computational framework that assesses various features of iCLIP data to provide quality control standards which reveals how technical variations between experiments affect the specificity of assigned binding sites. I used CLIPo to compare multiple PTBP1 experiments produced by iCLIP, eCLIP and irCLIP, to reveal major effects of sequence constraints at cDNA-ends or starts, cDNA length distribution and non-specific contaminants. Moreover, I assessed how the variations between these methods influence the mechanistic conclusions. Thus, CLIPo presents the quality control standards for transcriptome-wide assignment of protein-RNA binding sites. I continued with analyses of RBP complexes by using data from spliceosome iCLIP. This method simultaneously detects crosslink sites of small nuclear ribonucleo proteins (snRNPs) and auxiliary splicing factors on pre-mRNAs. I demonstrated that the high resolution of spliceosome-iCLIP allows for distinction between multiple proximal RNA binding sites, which can be valuable for transcriptome-wide studies of large ribonucleo protein complexes. Moreover, I showed that spliceosome-iCLIP can experimentally identify over 50,000 human branch points. In summary, I detected technical biases from iCLIP data, and demonstrated how such biases can be avoided, so that cDNA-starts appropriately assign the RNA binding sites. CLIPo analysis proved a useful quality control tool that evaluates data specificity across different methods, and I applied it to iCLIP, irCLIP and ENCODE eCLIP datasets. I presented how spliceosome-iCLIP data can be used to study the splicing machinery on pre-mRNAs and how to use constrained cDNAs from spliceosome-iCLIP data to identify branch points on a genome-wide scale. Taken together, these studies provide new insights into the field of RNA biology and can be used for future studies of iCLIP and related methods.
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Henricson, Anna. "Analyses of protein evolution, function, and architecture." Stockholm, 2010. http://diss.kib.ki.se/2010/978-91-7409-753-5/.

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Yan, Yongpan. "Computational analyses of microbial genomes operons, protein families and lateral gene transfer /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2596.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Cell Biology & Molecular Genetics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Rajapaksha, Suneth P. "Single Molecule Spectroscopy Studies of Membrane Protein Dynamics and Energetics by Combined Experimental and Computational Analyses." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1337141955.

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Chegancas, Rito Tiago Miguel. "Modelling and comparing protein interaction networks using subgraph counts." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:dcc0eb0d-1dd8-428d-b2ec-447a806d6aa8.

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The astonishing progress of molecular biology, engineering and computer science has resulted in mature technologies capable of examining multiple cellular components at a genome-wide scale. Protein-protein interactions are one example of such growing data. These data are often organised as networks with proteins as nodes and interactions as edges. Albeit still incomplete, there is now a substantial amount of data available and there is a need for biologically meaningful methods to analyse and interpret these interactions. In this thesis we focus on how to compare protein interaction networks (PINs) and on the rela- tionship between network architecture and the biological characteristics of proteins. The underlying theme throughout the dissertation is the use of small subgraphs – small interaction patterns between 2-5 proteins. We start by examining two popular scores that are used to compare PINs and network models. When comparing networks of the same model type we find that the typical scores are highly unstable and depend on the number of nodes and edges in the networks. This is unsatisfactory and we propose a method based on non-parametric statistics to make more meaningful comparisons. We also employ principal component analysis to judge model fit according to subgraph counts. From these analyses we show that no current model fits to the PINs; this may well reflect our lack of knowledge on the evolution of protein interactions. Thus, we use explanatory variables such as protein age and protein structural class to find patterns in the interactions and subgraphs we observe. We discover that the yeast PIN is highly heterogeneous and therefore no single model is likely to fit the network. Instead, we focus on ego-networks containing an initial protein plus its interacting partners and their interaction partners. In the final chapter we propose a new, alignment-free method for network comparison based on such ego-networks. The method compares subgraph counts in neighbourhoods within PINs in an averaging, many-to-many fashion. It clusters networks of the same model type and is able to successfully reconstruct species phylogenies solely based on PIN data providing exciting new directions for future research.
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Jonsson, Pall Freyr. "Computational analysis of protein-protein interaction networks." Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439848.

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Wang, Kai. "Novel computational methods for accurate quantitative and qualitative protein function prediction /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/11488.

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Ansari, Sam. "Analysis of protein-protein interactions : a computational approach /." Saarbrücken : VDM Verl. Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2992987&prov=M&dok_var=1&dok_ext=htm.

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Books on the topic "Computational analyses protein"

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Nannan, Gao, ed. Lecture notes on computational mutation. New York: Nova Science Publishers, 2008.

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András, Aszódi, ed. Protein geometry, classification, topology and symmetry: A computational analysis of structure. Bristol: Institute of Physics Pub., 2005.

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Suhai, Sándor. Genomics and proteomics: Functional and computational aspects. New York: Kluwer Academic Publishers, 2002.

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D, Baxevanis Andreas, and Ouellette B. F. Francis, eds. Bioinformatics: A practical guide to the analysis of genes and proteins. 3rd ed. Hoboken, N.J: Wiley, 2005.

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A, Andrade Miguel, ed. Bioinformatics and genomes: Current perspectives. Wymondham, England: Horizon Scientific, 2003.

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D, Higgins, and Taylor W. R, eds. Bioinformatics: Sequence, structure, and databanks : a practical approach. Oxford: Oxford University Press, 2000.

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D, Baxevanis Andreas, and Ouellette B. F. Francis, eds. Bioinformatics: A practical guide to the analysis of genes and proteins. 2nd ed. New York, NY: Wiley-Interscience, 2001.

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C, Hoch Jeffrey, Poulsen Flemming M, Redfield Christina, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on Computational Aspects of the Study of Biological Macromolecules by Nuclear Magnetic Resonance Spectroscopy (1990 : Il Ciocco, Italy), eds. Computational aspects of the study of biological macromolecules by nuclear magnetic resonance spectroscopy. New York: Plenum Press, 1991.

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Mather A. R. Sadiq Al-Baghdadi. CFD models for analysis and design of PEM fuel cells CFD models for analysis & design of PEM fuel cells. New York: Nova Science Publishers, 2008.

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Book chapters on the topic "Computational analyses protein"

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Dong, Shaowei, and Nicholas J. Provart. "Analyses of Protein Interaction Networks Using Computational Tools." In Methods in Molecular Biology, 97–117. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7871-7_7.

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Sljoka, Adnan. "Structural and Functional Analysis of Proteins Using Rigidity Theory." In Sublinear Computation Paradigm, 337–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4095-7_14.

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AbstractOver the past two decades, we have witnessed an unprecedented explosion in available biological data. In the age of big data, large biological datasets have created an urgent need for the development of bioinformatics methods and innovative fast algorithms. Bioinformatics tools can enable data-driven hypothesis and interpretation of complex biological data that can advance biological and medicinal knowledge discovery. Advances in structural biology and computational modelling have led to the characterization of atomistic structures of many biomolecular components of cells. Proteins in particular are the most fundamental biomolecules and the key constituent elements of all living organisms, as they are necessary for cellular functions. Proteins play crucial roles in immunity, catalysis, metabolism and the majority of biological processes, and hence there is significant interest to understand how these macromolecules carry out their complex functions. The mechanical heterogeneity of protein structures and a delicate mix of rigidity and flexibility, which dictates their dynamic nature, is linked to their highly diverse biological functions. Mathematical rigidity theory and related algorithms have opened up many exciting opportunities to accurately analyse protein dynamics and probe various biological enigmas at a molecular level. Importantly, rigidity theoretical algorithms and methods run in almost linear time complexity, which makes it suitable for high-throughput and big-data style analysis. In this chapter, we discuss the importance of protein flexibility and dynamics and review concepts in mathematical rigidity theory for analysing stability and the dynamics of protein structures. We then review some recent breakthrough studies, where we designed rigidity theory methods to understand complex biological events, such as allosteric communication, large-scale analysis of immune system antibody proteins, the highly complex dynamics of intrinsically disordered proteins and the validation of Nuclear Magnetic Resonance (NMR) solved protein structures.
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Nanni, Luca. "Computational Inference of DNA Folding Principles: From Data Management to Machine Learning." In Special Topics in Information Technology, 79–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_7.

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AbstractDNA is the molecular basis of life and would total about three meters if linearly untangled. To fit in the cell nucleus at the micrometer scale, DNA has, therefore, to fold itself into several layers of hierarchical structures, which are thought to be associated with functional compartmentalization of genomic features like genes and their regulatory elements. For this reason, understanding the mechanisms of genome folding is a major biological research problem. Studying chromatin conformation requires high computational resources and complex data analyses pipelines. In this chapter, we first present the PyGMQL software for interactive and scalable data exploration for genomic data. PyGMQL allows the user to inspect genomic datasets and design complex analysis pipelines. The software presents itself as a easy-to-use Python library and interacts seamlessly with other data analysis packages. We then use the software for the study of chromatin conformation data. We focus on the epigenetic determinants of Topologically Associating Domains (TADs), which are region of high self chromatin interaction. The results of this study highlight the existence of a “grammar of genome folding” which dictates the formation of TADs and boundaries, which is based on the CTCF insulator protein. Finally we focus on the relationship between chromatin conformation and gene expression, designing a graph representation learning model for the prediction of gene co-expression from gene topological features obtained from chromatin conformation data. We demonstrate a correlation between chromatin topology and co-expression, shedding a new light on this debated topic and providing a novel computational framework for the study of co-expression networks.
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Talevich, Eric, Natarajan Kannan, and Diego Miranda-Saavedra. "Computational Analysis of Apicomplexan Kinomes." In Protein Phosphorylation in Parasites, 1–36. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527675401.ch01.

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Linding, Rune, Ivica Letunic, Toby J. Gibson, and Peer Bork. "Computational Analysis of Modular Protein Architectures." In Modular Protein Domains, 439–76. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527603611.ch21.

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Poluri, Krishna Mohan, Khushboo Gulati, and Sharanya Sarkar. "Prediction, Analysis, Visualization, and Storage of Protein–Protein Interactions Using Computational Approaches." In Protein-Protein Interactions, 265–346. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1594-8_6.

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Barth, Marie, and Carla Schmidt. "Quantitative Cross-Linking of Proteins and Protein." In Methods in Molecular Biology, 385–400. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_26.

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AbstractCross-linking, in general, involves the covalent linkage of two amino acid residues of proteins or protein complexes in close proximity. Mass spectrometry and computational analysis are then applied to identify the formed linkage and deduce structural information such as distance restraints. Quantitative cross-linking coupled with mass spectrometry is well suited to study protein dynamics and conformations of protein complexes. The quantitative cross-linking workflow described here is based on the application of isotope labelled cross-linkers. Proteins or protein complexes present in different structural states are differentially cross-linked using a “light” and a “heavy” cross-linker. The intensity ratios of cross-links (i.e., light/heavy or heavy/light) indicate structural changes or interactions that are maintained in the different states. These structural insights lead to a better understanding of the function of the proteins or protein complexes investigated. The described workflow is applicable to a wide range of research questions including, for instance, protein dynamics or structural changes upon ligand binding.
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Yosef, Nir, Eytan Ruppin, and Roded Sharan. "Cross-Species Analysis of Protein-protein Interaction Networks." In Computational Biology, 163–85. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-125-1_9.

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Ge, Ruiquan, Qing Wu, and Jinbo Xu. "Computational Methods for Protein–Protein Interaction Network Alignment." In Recent Advances in Biological Network Analysis, 45–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57173-3_3.

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Schwämmle, Veit, and Marc Vaudel. "Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis." In Protein Bioinformatics, 437–58. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_21.

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Conference papers on the topic "Computational analyses protein"

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Halakou, Farideh, Attila Gursoy, Emel Sen Kilic, and Ozlem Keskin. "Topological, functional, and structural analyses of protein-protein interaction networks of breast cancer lung and brain metastases." In 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2017. http://dx.doi.org/10.1109/cibcb.2017.8058539.

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Nguyen, Nguyen, Xiaolin Zhang, Yunji Wang, Hai-Chao Han, Yufang Jin, Galen Schmidt, Richard A. Lange, Robert J. Chilton, and Merry Lindsey. "Targeting myocardial infarction-specific protein interaction network using computational analyses." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169479.

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Sharma, G., M. Badescu, A. Dubey, C. Mavroidis, T. Sessa, S. M. Tomassone, and M. L. Yarmush. "Kinematics and Workspace Analysis of Protein Based Nano-Motors." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57569.

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Kinematic and workspace analyses are performed to predict the performance of a new nanoscale biomolecular motor: The Viral Protein Linear (VPL) Motor. The motor is based on a conformational change observed in a family of viral envelope proteins when subjected to a changing pH environment. The conformational change produces a motion of about 10 nm, making the VPL a basic linear actuator, which can be further interfaced with other organic/inorganic nanoscale components such as DNA actuators and carbon nanotubes. This paper presents the principle of operation of the VPL motor and the development of direct and inverse kinematic models for workspace analysis. Preliminary results obtained from the developed computational tools are presented.
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Hyekyeong Kwon, Jang-Soo Chun, Zee-Yong Park, and Dong-Yu Kim. "Poster title (mass spectrometric protein profiling analyses of pathological and physiological hypertrophy cardiac muscle tissues)." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182652.

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Arikawa, Keisuke. "A Computational Framework for Predicting the Motions of a Protein System From a Robot Kinematics Viewpoint." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12527.

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There is an analogy between the kinematic structures of proteins and robotic mechanisms. On the basis of this analogy, we have so far developed some methods for predicting the internal motions of proteins from their three-dimensional structural data in protein data bank (PDB). However, these methods are basically applicable to a single protein molecule. In this study, we extended these methods to apply them to systems that consist of multiple molecules including proteins (protein systems), and developed a computational framework for predicting the motions of the molecules. The model used in this method is a type of elastic network model. In particular, proteins are modeled as a robot manipulator constrained by the springs (the dihedral angles on the main chains correspond to the joint angles). The interactions between molecules are also modeled as springs. The basic concept for predicting the motions is based on the analysis of structural compliance. By applying statically balanced forces to the model in various directions, we extracted those motions with larger structural compliance. To reduce the computational time, we formulated the method with the prospect of efficient computation including parallel computation. In addition, we developed a preparatory computer program implementing the proposed algorithms, and analyzed some protein systems. The results showed that the proposed computational framework can efficiently analyze large protein systems.
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Reeps, Christian, Michael W. Gee, Wolfgang A. Wall, and Hanns-Henning Eckstein. "Correlation of Biomechanics to Tissue Reaction in Aortic Aneurysm Assessed by Computational Finite Element Analysis and FDG-PET-CT." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19031.

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The widely accepted hypothesis that mechanobiological transduction plays a central role in pathogenesis of abdominal aortic aneurysm (AAA) is plausible, but so far was never directly proven in vivo for methodical reasons. At present, stresses and strains acting in AAA wall can be assessed by computational finite element analyses (FEA) [1,2,3]. Independently, it has also been reported that glycolytic activity in AAA wall non-invasively assessed by [18F]flourodeoxyglucose positron emission tomography/CT (FDG-PET/CT) is associated with increased proteolytic activity, structure-protein-degradation, AAA progression and consequently AAA wall instability as well as rupture risk [4]. Both methods were studied by our research group in an individual AAA patient [5]. Here, the correlation of computational biomechanics with metabolic activity assessed by FDG-PET/CT is analyzed in a larger patient cohort (n=18) [8].
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Honarmandi, Peyman, Philip Bransford, and Roger D. Kamm. "Mechanical Properties of α-Helices Estimated Using Molecular Dynamics and Finite Element Simulations." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-69058.

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Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.
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Shahbazi, Zahra, Horea T. Ilies¸, and Kazem Kazerounian. "Protein Molecules as Natural Nano Bio Devices: Mobility Analysis." In ASME 2010 First Global Congress on NanoEngineering for Medicine and Biology. ASMEDC, 2010. http://dx.doi.org/10.1115/nemb2010-13021.

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Proteins are nature’s nano-robots in the form of functional molecular components of living cells. The function of these natural nano-robots often requires conformational transitions between two or more native conformations that are made possible by the intrinsic mobility of the proteins. Understanding these transitions is essential to the understanding of how proteins function, as well as to the ability to design and manipulate protein-based nano-mechanical systems [1]. Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOF) with considerable computational implications. On the other hand, real protein molecules appear to exhibits a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of Hydrogen bonds during the folding process.
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Shahbazi, Zahra, Horea T. Ilies¸, and Kazem Kazerounian. "On Hydrogen Bonds and Mobility of Protein Molecules." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87470.

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Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOF) with considerable computational implications. On the other hand, real protein molecules appear to exhibits a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of Hydrogen bonds during the folding process. In this paper we explore the pivotal role of Hydrogen bonds in determining the structure and function of the proteins from the point of view of mechanical mobility. The existing geometric criteria on the formation of Hydrogen bonds are reviewed and a new set of geometric criteria are proposed. We show that the new criteria better correlate the number of predicted Hydrogen bonds with those established by biological principles than other existing criteria. Furthermore, we employ established tools in kinematics mobility analysis to evaluate the internal mobility of protein molecules, and to identify the rigid and flexible segments of the proteins. Our results show that the developed procedure significantly reduces the DOF of the protein models, with an average reduction of 94%. Such a dramatic reduction in the number of DOF can have has enormous computational implications in protein folding simulations.
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Kazerounian, Kazem, Khalid Latif, Kimberly Rodriguez, and Carlos Alvarado. "ProtoFold: Part I — Nanokinematics for Analysis of Protein Molecules." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57243.

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Proteins are evolution’s mechanisms of choice. Study of nano-mechanical systems must encompass an understanding of the geometry and conformation of protein molecules. Proteins are open or closed loop kinematic chains of miniature rigid bodies connected by revolute joints. The Kinematics community is in a unique position to extend the boundaries of knowledge in nano biomechanical systems. ProtoFold is a software package that implements novel and comprehensive methodologies for ab initio prediction of the final three-dimensional conformation of a protein, given only its linear structure. In this paper, we present the methods utilized in the kinematics notion and kinematics analysis of protein molecules. The kinematics portion of ProtoFold incorporates the Zero-Position Analysis Method and draws upon other recent advances in robot manipulation theories. We claim that the methodology presented is a computationally superior and more stable alternative to traditional molecular dynamics simulation techniques.
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Reports on the topic "Computational analyses protein"

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Gershoni, Jonathan M., David E. Swayne, Tal Pupko, Shimon Perk, Alexander Panshin, Avishai Lublin, and Natalia Golander. Discovery and reconstitution of cross-reactive vaccine targets for H5 and H9 avian influenza. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7699854.bard.

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Research objectives: Identification of highly conserved B-cell epitopes common to either H5 or H9 subtypes of AI Reconstruction of conserved epitopes from (1) as recombinantimmunogens, and testing their suitability to be used as universal vaccine components by measuring their binding to Influenza vaccinated sera of birds Vaccination of chickens with reconstituted epitopes and evaluation of successful vaccination, clinical protection and viral replication Development of a platform to investigate the dynamics of immune response towards infection or an epitope based vaccine Estimate our ability to focus the immune response towards an epitope-based vaccine using the tool we have developed in (D) Summary: This study is a multi-disciplinary study of four-way collaboration; The SERPL, USDA, Kimron-Israel, and two groups at TAU with the purpose of evaluating the production and implementation of epitope based vaccines against avian influenza (AI). Systematic analysis of the influenza viral spike led to the production of a highly conserved epitope situated at the hinge of the HA antigen designated “cluster 300” (c300). This epitope consists of a total of 31 residues and was initially expressed as a fusion protein of the Protein 8 major protein of the bacteriophagefd. Two versions of the c300 were produced to correspond to the H5 and H9 antigens respectively as well as scrambled versions that were identical with regard to amino acid composition yet with varied linear sequence (these served as negative controls). The recombinantimmunogens were produced first as phage fusions and then subsequently as fusions with maltose binding protein (MBP) or glutathioneS-transferase (GST). The latter were used to immunize and boost chickens at SERPL and Kimron. Furthermore, vaccinated and control chickens were challenged with concordant influenza strains at Kimron and SEPRL. Polyclonal sera were obtained for further analyses at TAU and computational bioinformatics analyses in collaboration with Prof. Pupko. Moreover, the degree of protection afforded by the vaccination was determined. Unfortunately, no protection could be demonstrated. In parallel to the main theme of the study, the TAU team (Gershoni and Pupko) designed and developed a novel methodology for the systematic analysis of the antibody composition of polyclonal sera (Deep Panning) which is essential for the analyses of the humoral response towards vaccination and challenge. Deep Panning is currently being used to monitor the polyclonal sera derived from the vaccination studies conducted at the SEPRL and Kimron.
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Rannenberg, Kai, Sebastian Pape, Frédéric Tronnier, and Sascha Löbner. Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector. Universitätsbibliothek Johann Christian Senckenberg, October 2021. http://dx.doi.org/10.21248/gups.63413.

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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