Relevant bibliographies by topics / Protein Structure Models

Contents

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

Academic literature on the topic 'Protein Structure Models'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Protein Structure Models"

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Mucsi, Z., Z. Gaspari, G. Orosz, and A. Perczel. "Structure-oriented rational design of chymotrypsin inhibitor models." Protein Engineering Design and Selection 16, no. 9 (September 1, 2003): 673–81. http://dx.doi.org/10.1093/protein/gzg090.

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Kim, HyungRae. "Residue Environment Score for Selecting Protein Structure Models and Protein-Protein Docking Models." Biophysical Journal 110, no. 3 (February 2016): 346a. http://dx.doi.org/10.1016/j.bpj.2015.11.1862.

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Koliński, A., and J. Skolnick. "High coordination lattice models of protein structure, dynamics and thermodynamics." Acta Biochimica Polonica 44, no. 3 (September 30, 1997): 389–422. http://dx.doi.org/10.18388/abp.1997_4393.

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A high coordination lattice discretization of protein conformational space is described. The model allows discrete representation of polypeptide chains of globular proteins and small macromolecular assemblies with an accuracy comparable to the accuracy of crystallographic structures. Knowledge based force field, that consists of sequence specific short range interactions, cooperative model of hydrogen bond network and tertiary one body, two body and multibody interactions, is outlined and discussed. A model of stochastic dynamics for these protein models is also described. The proposed method enables moderate resolution tertiary structure prediction of simple and small globular proteins. Its applicability in structure prediction increases significantly when evolutionary information is exploited or/and when sparse experimental data are available. The model responds correctly to sequence mutations and could be used at early stages of a computer aided protein design and protein redesign. Computational speed, associated with the discrete structure of the model, enables studies of the long time dynamics of polypeptides and proteins and quite detailed theoretical studies of thermodynamics of nontrivial protein models.
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Yang, Yifeng David, Preston Spratt, Hao Chen, Changsoon Park, and Daisuke Kihara. "Sub-AQUA: real-value quality assessment of protein structure models." Protein Engineering, Design and Selection 23, no. 8 (June 4, 2010): 617–32. http://dx.doi.org/10.1093/protein/gzq030.

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Kihara, Daisuke, Hao Chen, and Yifeng Yang. "Quality Assessment of Protein Structure Models." Current Protein & Peptide Science 10, no. 3 (June 1, 2009): 216–28. http://dx.doi.org/10.2174/138920309788452173.

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Hirsch, M., and M. Habeck. "Mixture models for protein structure ensembles." Bioinformatics 24, no. 19 (July 28, 2008): 2184–92. http://dx.doi.org/10.1093/bioinformatics/btn396.

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Ghosh, Soma, and Saraswathi Vishveshwara. "Ranking the quality of protein structure models using sidechain based network properties." F1000Research 3 (January 21, 2014): 17. http://dx.doi.org/10.12688/f1000research.3-17.v1.

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Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server(http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.
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Domingues, F. S., J. Rahnenfuhrer, and T. Lengauer. "Automated clustering of ensembles of alternative models in protein structure databases." Protein Engineering Design and Selection 17, no. 6 (August 3, 2004): 537–43. http://dx.doi.org/10.1093/protein/gzh063.

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Shatabda, Swakkhar, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham, and Abdul Sattar. "How Good Are Simplified Models for Protein Structure Prediction?" Advances in Bioinformatics 2014 (April 29, 2014): 1–9. http://dx.doi.org/10.1155/2014/867179.

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Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP.
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Bienkowska, J., Hongxian He, and T. F. Smith. "Automatic pattern embedding in protein structure models." IEEE Intelligent Systems 16, no. 6 (November 2001): 21–25. http://dx.doi.org/10.1109/5254.972074.

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Dissertations / Theses on the topic "Protein Structure Models"

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Simons, Kim T. "Deciphering the protein folding code : ab initio prediction of protein structure /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/9234.

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Griffiths-Jones, Samuel R. "Peptide models for protein beta-sheets." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364650.

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Wróblewska, Liliana. "Refinement of reduced protein models with all-atom force fields." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/26606.

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The goal of the following thesis research was to develop a systematic approach for the refinement of low-resolution protein models, as a part of the protein structure prediction procedure. Significant progress has been made in the field of protein structure prediction and the contemporary methods are able to assemble correct topology for a large fraction of protein domains. But such approximate models are often not detailed enough for some important applications, including studies of reaction mechanisms, functional annotation, drug design or virtual ligand screening. The development of a method that could bring those structures closer to the native is then of great importance. The minimal requirements for a potential that can refine protein structures is the existence of a correlation between the energy with native similarity and the scoring of the native structure as being lowest in energy. Extensive tests of the contemporary all-atom physics-based force fields were conducted to assess their applicability for refinement. The tests revealed flatness of such potentials and enabled the identification of the key problems in the current approaches. Guided by these results, the optimization of the AMBER (ff03) force field was performed that aimed at creating a funnel shape of the potential, with the native structure at the global minimum. Such shape should facilitate the conformational search during refinement and drive it towards the native conformation. Adjusting the relative weights of particular energy components, and adding an explicit hydrogen bond potential significantly improved the average correlation coefficient of the energy with native similarity (from 0.25 for the original ff03 potential to 0.65 for the optimized force field). The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. The new, optimized potential was subsequently used to refine protein models of various native-similarity. The test employed 47 proteins and 100 decoy structures per protein. When the lowest energy structure from each trajectory was compared with the starting decoy, we observed structural improvement for 70% of the models on average. Such an unprecedented result of a systematic refinement is extremely promising in the context of high-resolution structure prediction.
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Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.

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Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.

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Käll, Lukas. "Predicting transmembrane topology and signal peptides with hidden Markov models /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-719-7/.

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Tångrot, Jeanette. "Structural Information and Hidden Markov Models for Biological Sequence Analysis." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1629.

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Bioinformatics is a fast-developing field, which makes use of computational methods to analyse and structure biological data. An important branch of bioinformatics is structure and function prediction of proteins, which is often based on finding relationships to already characterized proteins. It is known that two proteins with very similar sequences also share the same 3D structure. However, there are many proteins with similar structures that have no clear sequence similarity, which make it difficult to find these relationships. In this thesis, two methods for annotating protein domains are presented, one aiming at assigning the correct domain family or families to a protein sequence, and the other aiming at fold recognition. Both methods use hidden Markov models (HMMs) to find related proteins, and they both exploit the fact that structure is more conserved than sequence, but in two different ways. Most of the research presented in the thesis focuses on the structure-anchored HMMs, saHMMs. For each domain family, an saHMM is constructed from a multiple structure alignment of carefully selected representative domains, the saHMM-members. These saHMM-members are collected in the so called "midnight ASTRAL set", and are chosen so that all saHMM-members within the same family have mutual sequence identities below a threshold of about 20%. In order to construct the midnight ASTRAL set and the saHMMs, a pipe-line of software tools are developed. The saHMMs are shown to be able to detect the correct family relationships at very high accuracy, and perform better than the standard tool Pfam in assigning the correct domain families to new domain sequences. We also introduce the FI-score, which is used to measure the performance of the saHMMs, in order to select the optimal model for each domain family. The saHMMs are made available for searching through the FISH server, and can be used for assigning family relationships to protein sequences. The other approach presented in the thesis is secondary structure HMMs (ssHMMs). These HMMs are designed to use both the sequence and the predicted secondary structure of a query protein when scoring it against the model. A rigorous benchmark is used, which shows that HMMs made from multiple sequences result in better fold recognition than those based on single sequences. Adding secondary structure information to the HMMs improves the ability of fold recognition further, both when using true and predicted secondary structures for the query sequence.
Bioinformatik är ett område där datavetenskapliga och statistiska metoder används för att analysera och strukturera biologiska data. Ett viktigt område inom bioinformatiken försöker förutsäga vilken tredimensionell struktur och funktion ett protein har, utifrån dess aminosyrasekvens och/eller likheter med andra, redan karaktäriserade, proteiner. Det är känt att två proteiner med likande aminosyrasekvenser också har liknande tredimensionella strukturer. Att två proteiner har liknande strukturer behöver dock inte betyda att deras sekvenser är lika, vilket kan göra det svårt att hitta strukturella likheter utifrån ett proteins aminosyrasekvens. Den här avhandlingen beskriver två metoder för att hitta likheter mellan proteiner, den ena med fokus på att bestämma vilken familj av proteindomäner, med känd 3D-struktur, en given sekvens tillhör, medan den andra försöker förutsäga ett proteins veckning, d.v.s. ge en grov bild av proteinets struktur. Båda metoderna använder s.k. dolda Markov modeller (hidden Markov models, HMMer), en statistisk metod som bland annat kan användas för att beskriva proteinfamiljer. Med hjälp en HMM kan man förutsäga om en viss proteinsekvens tillhör den familj modellen representerar. Båda metoderna använder också strukturinformation för att öka modellernas förmåga att känna igen besläktade sekvenser, men på olika sätt. Det mesta av arbetet i avhandlingen handlar om strukturellt förankrade HMMer (structure-anchored HMMs, saHMMer). För att bygga saHMMerna används strukturbaserade sekvensöverlagringar, vilka genereras utifrån hur proteindomänerna kan läggas på varandra i rymden, snarare än utifrån vilka aminosyror som ingår i deras sekvenser. I varje proteinfamilj används bara ett särskilt, representativt urval av domäner. Dessa är valda så att då sekvenserna jämförs parvis, finns det inget par inom familjen med högre sekvensidentitet än ca 20%. Detta urval görs för att få så stor spridning som möjligt på sekvenserna inom familjen. En programvaruserie har utvecklats för att välja ut representanter för varje familj och sedan bygga saHMMer baserade på dessa. Det visar sig att saHMMerna kan hitta rätt familj till en hög andel av de testade sekvenserna, med nästan inga fel. De är också bättre än den ofta använda metoden Pfam på att hitta rätt familj till helt nya proteinsekvenser. saHMMerna finns tillgängliga genom FISH-servern, vilken alla kan använda via Internet för att hitta vilken familj ett intressant protein kan tillhöra. Den andra metoden som presenteras i avhandlingen är sekundärstruktur-HMMer, ssHMMer, vilka är byggda från vanliga multipla sekvensöverlagringar, men också från information om vilka sekundärstrukturer proteinsekvenserna i familjen har. När en proteinsekvens jämförs med ssHMMen används en förutsägelse om sekundärstrukturen, och den beräknade sannolikheten att sekvensen tillhör familjen kommer att baseras både på sekvensen av aminosyror och på sekundärstrukturen. Vid en jämförelse visar det sig att HMMer baserade på flera sekvenser är bättre än sådana baserade på endast en sekvens, när det gäller att hitta rätt veckning för en proteinsekvens. HMMerna blir ännu bättre om man också tar hänsyn till sekundärstrukturen, både då den riktiga sekundärstrukturen används och då man använder en teoretiskt förutsagd.
Jeanette Hargbo.
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Pettitt, Christopher Steven. "Refinement of protein structure models with multi-objective genetic algorithms." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446043/.

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Here I investigate the protein structure refinement problem for homology-based protein structure models. The refinement problem has been identified as a major bottleneck in the structure prediction process and inhibits the goal of producing high-resolution experimental quality structures for target protein sequences. This thesis is composed of three investigations into aspects of template-based modelling and refinement. In the primary investigation, empirical evidence is provided to support the hypothesis that using multiple template-based structures to model a target sequence can improve the quality of the prediction over that obtained solely by using the single best prediction. A multi-objective genetic algorithm is used to optimize protein structure models by using the structural information from a set of predictions, guided by various objective functions. The effect of multi-objective optimization on model quality is examined. A benchmark of energy functions and model quality assessment methods is performed in the context of automated homology modelling to assess the ability of these methods at discriminating nearer-native structures from a set of predictions. These model quality assessment methods were unable to significantly improve the ranking of threading- based prediction methods though some model quality assessment methods improved model selection for methods which use sequence information alone. The results suggest that structural informational can provide valuable information for distinguishing better models where only sequence information has been used for modelling. The suitability of these energy functions for high-resolution refinement is discussed. Finally, a stochastic optimization algorithm is developed for refining homology-based protein structure models using evolutionary algorithms. This approach uses multiple structural model inputs, conformational sampling operators, and objective functions for guiding a search through conformational space. Single- and multi-objective genetic variants are applied to homology model predictions for 35 target proteins. The refinement results are discussed and the performance of both algorithmic variants compared and contrasted.
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Hayward, Steven John. "Studies in protein secondary structure prediction with neural network models." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14034.

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The aim of this work was to predict protein secondary structure using neural network models. Initially a Hopfield network was used but abandoned in favour of a layered network trained using the back propagation algorithm. In the early stages of this work an exploration of the many different approaches to this problem was undertaken. These included attempts to predict boundaries between secondary structures, the secondary structures of individual residues, and the secondary structures of sequences wholly within a particular secondary structure. Results indicated the latter to be the best approach to continue with. In addition two coding schemes were investigated: a coding scheme based on occurrences of pairs of residues and one based on the positions of residues. It was found that this positional coding scheme was the natural coding scheme for this problem. On segments of whole alpha-helix and whole non-alpha-helix 10 residues in length a prediction success of around 80% with a correlation coefficient of 0.52 was achieved with the positional coding scheme. On whole proteins, where predictions are made for individual residues, alpha-helix prediction drops to 73% with a correlation coefficient of 0.34. The relative predictability of alpha-helices of above and below average accessibility was also investigated showing that those of above average accessibility are more predictable than those with below average accessibility. The main body of this work concerns the apparent limit of predictability of alpha-helices. It was found that test set prediction did not depend on the number of hidden nodes. In fact, a single layer network performed as well as those with hidden nodes showing that the probolem is basically linearly separable. In addition, prediction success plateaus well below that of perfect prediction success. During training, test set prediction is seen to peak. The decrease in prediction success was found to be due to non-alpha-helix sequences that the network was unable to distinguish from real alpha-helix sequences. These regions of non-alpha-helix were shown to occur adjacent to actual alpha-helices with statistical significance. It is proposed that potential alpha-helices are disrupted by global constraints during the formation of tertiary structure. The effect of window size was also investigated as was beta-sheet prediction, but this was found to be limited by the small number of examples available with our approach. However, its distribution in the input space in relation to alpha-helix and coil was determined.
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Gregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.

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The three-dimensional structure of a protein, formed as a result of amino-acid sequences folding into compact domains, is regarded as a key factor in its biological function. How and why proteins fold into specific topologies, remain the key focus of scientific research in the field of biophysics. By stripping down complex reactions down to the most basic elements, biophysicists aim to develop simplified models for biological phenomena such as antibody discrimination, viral fusion or self-assembly. Focusing on small model peptide systems, rather than the full proteins from which they were derived, was hoped to result in accurate structural measurements and provide a more transparent comparison between simulation and experiment. The aim of this research was therefore to investigate how accurate these models were when compared against experiment. Furthermore, while breaking down the complex biological phenomena into simple models, there was also a conscious effort to ensure that the models were representative of real biological systems, and a major focus was therefore aimed at determining whether any meaningful biomedical insight may be extrapolated from such models. Peptides found in hormones (human chorionic gonadotropin, luteinizing hormone), viruses (HIV) and amyloid diseases (transthyretin) were selected in order to probe a variety of questions in relation to the aforementioned biological phenomena. Namely, how the primary sequence influenced the three-dimensional structure (and thus its biological function), how its environment could influence such a confirmation, and how these systems aggregated. This doctoral study has made use of a combination of computer simulations and experimental techniques to investigate a selection of biologically relevant peptides; utilising classical atomistic molecular dynamics (MD) simulations to characterise the free-energy landscapes of the chosen peptides, and compare these findings with the secondary structure content predicted by spectroscopic methods such as circular dichroism and infrared spectroscopy. The peptide systems studied within, were found to be characterised by rugged free-energy landscapes unlike their protein counterparts (defined by singular, deep minima). Furthermore, these landscapes were found to be highly plastic and sensitive to changes in the local environment.
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Chippington-Derrick, T. C. "Models, methods and algorithms for constraint dynamics simulations of long chain molecules." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234776.

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Books on the topic "Protein Structure Models"

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Protein structure prediction. 3rd ed. New York: Humana Press, 2014.

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Henrik, Bohr, and Brunak S[0]ren, eds. Protein structure by distance analysis. Amsterdam: IOS Press, 1994.

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D, Fasman Gerald, ed. Prediction of protein structure and the principles of protein conformation. New York: Plenum Press, 1989.

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D, Fasman Gerald, ed. Prediction of protein structure and the principles of protein confirmation. New York: Plenum, 1989.

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1964-, Liang Jie, Xu Ying 1960-, and Xu Dong 1965-, eds. Computational methods for protein structure prediction and modeling. New York, N.Y: Springer, 2007.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Rangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.

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Rigden, Daniel John. From protein structure to function with bioinformatics. [Dordrecht]: Springer, 2009.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.

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Book chapters on the topic "Protein Structure Models"

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Bystroff, Christopher, and Anders Krogh. "Hidden Markov Models for Prediction of Protein Features." In Protein Structure Prediction, 173–98. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_7.

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Masso, Majid, and Iosif I. Vaisman. "Structure-Based Machine Learning Models for Computational Mutagenesis." In Introduction to Protein Structure Prediction, 403–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch18.

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Tautermann, Christofer S. "Target Based Virtual Screening by Docking into Automatically Generated GPCR Models." In Membrane Protein Structure and Dynamics, 255–70. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_15.

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Kihara, Daisuke, Yifeng David Yang, and Hao Chen. "Error Estimation of Template-Based Protein Structure Models." In Multiscale Approaches to Protein Modeling, 295–314. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6889-0_13.

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Frauenfelder, Hans. "Heme Protein Reactions: Models, Concepts, and Problems." In Structure, Dynamics and Function of Biomolecules, 10–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-71705-5_3.

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Isin, Basak, Kalyan C. Tirupula, Zoltán N. Oltvai, Judith Klein-Seetharaman, and Ivet Bahar. "Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation." In Membrane Protein Structure and Dynamics, 285–317. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_17.

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Rigden, Daniel J., Iwona A. Cymerman, and Janusz M. Bujnicki. "Prediction of Protein Function from Theoretical Models." In From Protein Structure to Function with Bioinformatics, 467–98. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3_15.

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Blaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Mateusz Kurcinski, Michal Kolinski, Maciej Pawel Ciemny, Katarzyna Ziolkowska, Marta Panek, and Andrzej Kolinski. "Protein Structure Prediction Using Coarse-Grained Models." In Springer Series on Bio- and Neurosystems, 27–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95843-9_2.

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Hansen-Goos, Hendrik, and Seth Lichter. "Geometric Models of Protein Secondary-Structure Formation." In Nano and Cell Mechanics, 411–35. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118482568.ch16.

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Blaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Katarzyna Ziolkowska, Marta Panek, and Andrzej Kolinski. "Coarse-Grained Protein Models in Structure Prediction." In Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes, 25–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-28554-7_2.

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Conference papers on the topic "Protein Structure Models"

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Winter, Pawel, and Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.

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"DIFFERENTIAL EVOLUTION TO MULTI-OBJECTIVE PROTEIN STRUCTURE PREDICTION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003767002950298.

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"STUDY OF PROTEIN STRUCTURE ALIGNMENT PROBLEM IN PARAMETERIZED COMPUTATION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003769701740181.

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"A Hybrid Local Search for Simplified Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004239401580163.

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RAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO, and ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS." In Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.

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Arora, Bhumika. "Refinement of G protein-coupled receptor structure models." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3414920.

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Arikawa, Keisuke. "Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47010.

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On the basis of robot kinematics, we have thus far developed a method for predicting the motion of proteins from their 3D structural data given in the Protein Data Bank (PDB data). In this method, proteins are modeled as serial manipulators constrained by springs and the structural compliance properties of the models are evaluated. We focus on localized instead of whole structures of proteins. Employing the same model used in our method of motion prediction, the motion properties of the localized structures and the relation between the motion properties of localized and whole structures are analyzed. First, we present a method for graphically expressing the deformation of objects with a complex shape, such as proteins, by approximating the shape as a rectangular prism with a mesh on its surface. We then formulate a method for comparing the motion properties of localized structures cleaved from the whole structure and those remaining in it by expressing the motion of the latter using the decomposed motion modes of the former according to the structural compliance. Finally, we show a method for evaluating the effect of a localized structure on the motion properties of proteins by applying forces to localized structures. In the formulations, we demonstrate applications as illustrative examples using the PDB data of a real protein.
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"BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003770801880194.

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Chu, Jia-Han, Chun Yuan Lin, Cheng-Wen Chang, Chihan Lee, Yuh-Shyong Yang, Chuan Yi Tang, Tuan D. Pham, and Xiaobo Zhou. "TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816621.

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Antczak, Piotr Lukasiak Maciej, Tomasz Ratajczak, Piotr Lukasiak, and Jacek Blazewicz. "SphereGrinder - reference structure-based tool for quality assessment of protein structural models." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359765.

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Reports on the topic "Protein Structure Models"

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Hart, W. E., and S. Istrail. Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/425317.

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Toda, Magdalena, and Bhagya Athukorallage. Geometric Models for Secondary Structures in Proteins. GIQ, 2015. http://dx.doi.org/10.7546/giq-16-2015-282-300.

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Christopher, David A., and Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, May 2004. http://dx.doi.org/10.32747/2004.7586534.bard.

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Original Objectives: 1. Purify and biochemically characterize RB60 orthologs in higher plant chloroplasts; 2. Clone the gene(s) encoding plant RB60 orthologs and determine their structure and expression; 3. Manipulate the expression of RB60; 4. Assay the effects of altered RB60 expression on thylakoid biogenesis and photosynthetic function in plants exposed to different light conditions. In addition, we also examined the gene structure and expression of RB60 orthologs in the non-vascular plant, Physcomitrella patens and cloned the poly(A)-binding protein orthologue (43 kDa RB47-like protein). This protein is believed to a partner that interacts with RB60 to bind to the psbA5' UTR. Thus, to obtain a comprehensive view of RB60 function requires analysis of its biochemical partners such as RB43. Background & Achievements: High levels of sunlight reduce photosynthesis in plants by damaging the photo system II reaction center (PSII) subunits, such as D1 (encoded by the chloroplast tpsbAgene). When the rate of D1 synthesis is less than the rate of photo damage, photo inhibition occurs and plant growth is decreased. Plants use light-activated translation and enhanced psbAmRNA stability to maintain D1 synthesis and replace the photo damaged 01. Despite the importance to photosynthetic capacity, these mechanisms are poorly understood in plants. One intriguing model derived from the algal chloroplast system, Chlamydomonas, implicates the role of three proteins (RB60, RB47, RB38) that bind to the psbAmRNA 5' untranslated leader (5' UTR) in the light to activate translation or enhance mRNA stability. RB60 is the key enzyme, protein D1sulfide isomerase (Pill), that regulates the psbA-RN :Binding proteins (RB's) by way of light-mediated redox potentials generated by the photosystems. However, proteins with these functions have not been described from higher plants. We provided compelling evidence for the existence of RB60, RB47 and RB38 orthologs in the vascular plant, Arabidopsis. Using gel mobility shift, Rnase protection and UV-crosslinking assays, we have shown that a dithiol redox mechanism which resembles a Pill (RB60) activity regulates the interaction of 43- and 30-kDa proteins with a thermolabile stem-loop in the 5' UTR of the psbAmRNA from Arabidopsis. We discovered, in Arabidopsis, the PD1 gene family consists of II members that differ in polypeptide length from 361 to 566 amino acids, presence of signal peptides, KDEL motifs, and the number and positions of thioredoxin domains. PD1's catalyze the reversible formation an disomerization of disulfide bonds necessary for the proper folding, assembly, activity, and secretion of numerous enzymes and structural proteins. PD1's have also evolved novel cellular redox functions, as single enzymes and as subunits of protein complexes in organelles. We provide evidence that at least one Pill is localized to the chloroplast. We have used PDI-specific polyclonal and monoclonal antisera to characterize the PD1 (55 kDa) in the chloroplast that is unevenly distributed between the stroma and pellet (containing membranes, DNA, polysomes, starch), being three-fold more abundant in the pellet phase. PD1-55 levels increase with light intensity and it assembles into a high molecular weight complex of ~230 kDa as determined on native blue gels. In vitro translation of all 11 different Pill's followed by microsomal membrane processing reactions were used to differentiate among PD1's localized in the endoplasmic reticulum or other organelles. These results will provide.1e insights into redox regulatory mechanisms involved in adaptation of the photosynthetic apparatus to light stress. Elucidating the genetic mechanisms and factors regulating chloroplast photosynthetic genes is important for developing strategies to improve photosynthetic efficiency, crop productivity and adaptation to high light environments.
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Elbaum, Michael, and Peter J. Christie. Type IV Secretion System of Agrobacterium tumefaciens: Components and Structures. United States Department of Agriculture, March 2013. http://dx.doi.org/10.32747/2013.7699848.bard.

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Objectives: The overall goal of the project was to build an ultrastructural model of the Agrobacterium tumefaciens type IV secretion system (T4SS) based on electron microscopy, genetics, and immunolocalization of its components. There were four original aims: Aim 1: Define the contributions of contact-dependent and -independent plant signals to formation of novel morphological changes at the A. tumefaciens polar membrane. Aim 2: Genetic basis for morphological changes at the A. tumefaciens polar membrane. Aim 3: Immuno-localization of VirB proteins Aim 4: Structural definition of the substrate translocation route. There were no major revisions to the aims, and the work focused on the above questions. Background: Agrobacterium presents a unique example of inter-kingdom gene transfer. The process involves cell to cell transfer of both protein and DNA substrates via a contact-dependent mechanism akin to bacterial conjugation. Transfer is mediated by a T4SS. Intensive study of the Agrobacterium T4SS has made it an archetypal model for the genetics and biochemistry. The channel is assembled from eleven protein components encoded on the B operon in the virulence region of the tumor-inducing plasmid, plus an additional coupling protein, VirD4. During the course of our project two structural studies were published presenting X-ray crystallography and three-dimensional reconstruction from electron microscopy of a core complex of the channel assembled in vitro from homologous proteins of E. coli, representing VirB7, VirB9, and VirB10. Another study was published claiming that the secretion channels in Agrobacterium appear on helical arrays around the membrane perimeter and along the entire length of the bacterium. Helical arrangements in bacterial membranes have since fallen from favor however, and that finding was partially retracted in a second publication. Overall, the localization of the T4SS within the bacterial membranes remains enigmatic in the literature, and we believe that our results from this project make a significant advance. Summary of achievements : We found that polar inflations and other membrane disturbances relate to the activation conditions rather than to virulence protein expression. Activation requires low pH and nutrient-poor medium. These stress conditions are also reflected in DNA condensation to varying degrees. Nonetheless, they must be considered in modeling the T4SS as they represent the relevant conditions for its expression and activity. We identified the T4SS core component VirB7 at native expression levels using state of the art super-resolution light microscopy. This marker of the secretion system was found almost exclusively at the cell poles, and typically one pole. Immuno-electron microscopy identified the protein at the inner membrane, rather than at bridges across the inner and outer membranes. This suggests a rare or transient assembly of the secretion-competent channel, or alternatively a two-step secretion involving an intermediate step in the periplasmic space. We followed the expression of the major secreted effector, VirE2. This is a single-stranded DNA binding protein that forms a capsid around the transferred oligonucleotide, adapting the bacterial conjugation to the eukaryotic host. We found that over-expressed VirE2 forms filamentous complexes in the bacterial cytoplasm that could be observed both by conventional fluorescence microscopy and by correlative electron cryo-tomography. Using a non-retentive mutant we observed secretion of VirE2 from bacterial poles. We labeled the secreted substrates in vivo in order detect their secretion and appearance in the plant cells. However the low transfer efficiency and significant background signal have so far hampered this approach.
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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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Kirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.

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In this project, we studied the photosynthetic apparatus during dehydration and rehydration of the homoiochlorophyllous resurrection plant Craterostigmapumilum (retains most of the photosynthetic components during desiccation). Resurrection plants have the remarkable capability to withstand desiccation, being able to revive after prolonged severe water deficit in a few days upon rehydration. Homoiochlorophyllous resurrection plants are very efficient in protecting the photosynthetic machinery against damage by reactive oxygen production under drought. The main purpose of this BARD project was to unravel these largely unknown protection strategies for C. pumilum. In detail, the specific objectives were: (1) To determine the distribution and local organization of photosynthetic protein complexes and formation of inverted hexagonal phases within the thylakoid membranes at different dehydration/rehydration states. (2) To determine the 3D structure and characterize the geometry, topology, and mechanics of the thylakoid network at the different states. (3) Generation of molecular models for thylakoids at the different states and study the implications for diffusion within the thylakoid lumen. (4) Characterization of inter-system electron transport, quantum efficiencies, photosystem antenna sizes and distribution, NPQ, and photoinhibition at different hydration states. (5) Measuring the partition of photosynthetic reducing equivalents between the Calvin cycle, photorespiration, and the water-water cycle. At the beginning of the project, we decided to use C. pumilum instead of C. wilmsii because the former species was available from our collaborator Dr. Farrant. In addition to the original two dehydration states (40 relative water content=RWC and 5% RWC), we characterized a third state (15-20%) because some interesting changes occurs at this RWC. Furthermore, it was not possible to detect D1 protein levels by Western blot analysis because antibodies against other higher plants failed to detect D1 in C. pumilum. We developed growth conditions that allow reproducible generation of different dehydration and rehydration states for C. pumilum. Furthermore, advanced spectroscopy and microscopy for C. pumilum were established to obtain a detailed picture of structural and functional changes of the photosynthetic apparatus in different hydrated states. Main findings of our study are: 1. Anthocyan accumulation during desiccation alleviates the light pressure within the leaves (Fig. 1). 2. During desiccation, stomatal closure leads to drastic reductions in CO2 fixation and photorespiration. We could not identify alternative electron sinks as a solution to reduce ROS production. 3. On the supramolecular level, semicrystalline protein arrays were identified in thylakoid membranes in the desiccated state (see Fig. 3). On the electron transport level, a specific series of shut downs occur (summarized in Fig. 2). The main events include: Early shutdown of the ATPase activity, cessation of electron transport between cyt. bf complex and PSI (can reduce ROS formation at PSI); at higher dehydration levels uncoupling of LHCII from PSII and cessation of electron flow from PSII accompanied by crystal formation. The later could severe as a swift PSII reservoir during rehydration. The specific order of events in the course of dehydration and rehydration discovered in this project is indicative for regulated structural transitions specifically realized in resurrection plants. This detailed knowledge can serve as an interesting starting point for rationale genetic engineering of drought-tolerant crops.
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Nelson, Nathan, and Charles F. Yocum. Structure, Function and Utilization of Plant Photosynthetic Reaction Centers. United States Department of Agriculture, September 2012. http://dx.doi.org/10.32747/2012.7699846.bard.

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Light capturing and energy conversion by PSI is one of the most fundamental processes in nature. In the heart of these adaptations stand PSI, PSII and their light harvesting antenna complexes. The main goal of this grant proposal was to obtain by X-ray crystallography information on the structure of plant photosystem I (PSI) and photosystem II (PSII) supercomplexes. We achieved several milestones along this line but as yet, like several strong laboratories around the world, we have no crystal structure of plant PSII. We have redesigned the purification and crystallization procedures and recently solved the crystal structure of the PSI supercomplex at 3.3 Å resolution. Even though this advance in resolution appears to be relatively small, we obtained a significantly improved model of the supercomplex. The work was published in J. Biol. Chem. (Amunts et al., 2010). The improved electron density map yielded identification and tracing of the PsaK subunit. The location of an additional 10 ß-carotenes, as well as 5 chlorophylls and several loop regions that were previously uninterruptable have been modeled. This represents the most complete plant PSI structure obtained thus far, revealing the locations of and interactions among 17 protein subunits and 193 non-covalently bound photochemical cofactors. We have continued extensive experimental efforts to improve the structure of plant PSI and to obtain PSII preparation amenable to crystallization. Most of our efforts were devoted to obtain well-defined subcomplexes of plant PSII preparations that are amenable to crystallization. We studied the apparent paradox of the high sensitivity of oxygen evolution of isolated thylakoids while BBY particles exhibit remarkable resilience to the same treatment. The integrity of the photosystem II (PSII) extrinsic protein complement as well as calcium effects arise from the Ca2+ atom associated with the site of photosynthetic water oxidation were investigated. This work provides deeper insights into the interaction of PsbO with PSII. Sight-directed mutagenesis indicated the location of critical sites involved in the stability of the water oxidation reaction. When combined with previous results, the data lead to a more detailed model for PsbO binding in eukaryotic PSII.
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Epel, Bernard, and Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, November 2005. http://dx.doi.org/10.32747/2005.7695874.bard.

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To cause disease, plant viruses must replicate and spread locally and systemically within the host. Cell-to-cell virus spread is mediated by virus-encoded movement proteins (MPs), which modify the structure and function of plasmodesmata (Pd), trans-wall co-axial membranous tunnels that interconnect the cytoplasm of neighboring cells. Tobacco mosaic virus (TMV) employ a single MP for cell- cell spread and for which CP is not required. The PIs, Beachy (USA) and Epel (Israel) and co-workers, developed new tools and approaches for study of the mechanism of spread of TMV that lead to a partial identification and molecular characterization of the cellular machinery involved in the trafficking process. Original research objectives: Based on our data and those of others, we proposed a working model of plant viral spread. Our model stated that MPᵀᴹⱽ, an integral ER membrane protein with its C-terminus exposed to the cytoplasm (Reichel and Beachy, 1998), alters the Pd SEL, causes the Pd cytoplasmic annulus to dilate (Wolf et al., 1989), allowing ER to glide through Pd and that this gliding is cytoskeleton mediated. The model claimed that in absence of MP, the ER in Pd (the desmotubule) is stationary, i.e. does not move through the Pd. Based on this model we designed a series of experiments to test the following questions: -Does MP potentiate ER movement through the Pd? - In the presence of MP, is there communication between adjacent cells via ER lumen? -Does MP potentiate the movement of cytoskeletal elements cell to cell? -Is MP required for cell-to-cell movement of ER membranes between cells in sink tissue? -Is the binding in situ of MP to RNA specific to vRNA sequences or is it nonspecific as measured in vitro? And if specific: -What sequences of RNA are involved in binding to MP? And finally, what host proteins are associated with MP during intracellular targeting to various subcellular targets and what if any post-translational modifications occur to MP, other than phosphorylation (Kawakami et al., 1999)? Major conclusions, solutions and achievements. A new quantitative tool was developed to measure the "coefficient of conductivity" of Pd to cytoplasmic soluble proteins. Employing this tool, we measured changes in Pd conductivity in epidermal cells of sink and source leaves of wild-type and transgenic Nicotiana benthamiana (N. benthamiana) plants expressing MPᵀᴹⱽ incubated both in dark and light and at 16 and 25 ᵒC (Liarzi and Epel, 2005 (appendix 1). To test our model we measured the effect of the presence of MP on cell-to-cell spread of a cytoplasmic fluorescent probe, of two ER intrinsic membrane protein-probes and two ER lumen protein-probes fused to GFP. The effect of a mutant virus that is incapable of cell-to-cell spread on the spread of these probes was also determined. Our data shows that MP reduces SEL for cytoplasmic molecules, dilates the desmotubule allowing cell-cell diffusion of proteins via the desmotubule lumen and reduces the rate of spread of the ER membrane probes. Replicase was shown to enhance cell-cell spread. The data are not in support of the proposed model and have led us to propose a new model for virus cell-cell spread: this model proposes that MP, an integral ER membrane protein, forms a MP:vRNAER complex and that this ER-membrane complex diffuses in the lipid milieu of the ER into the desmotubule (the ER within the Pd), and spreads cell to cell by simple diffusion in the ER/desmotubule membrane; the driving force for spread is the chemical potential gradient between an infected cell and contingent non-infected neighbors. Our data also suggests that the virus replicase has a function in altering the Pd conductivity. Transgenic plant lines that express the MP gene of the Cg tobamovirus fused to YFP under the control the ecdysone receptor and methoxyfenocide ligand were generated by the Beachy group and the expression pattern and the timing and targeting patterns were determined. A vector expressing this MPs was also developed for use by the Epel lab . The transgenic lines are being used to identify and isolate host genes that are required for cell-to-cell movement of TMV/tobamoviruses. This line is now being grown and to be employed in proteomic studies which will commence November 2005. T-DNA insertion mutagenesis is being developed to identify and isolate host genes required for cell-to-cell movement of TMV.
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Chamovitz, Daniel A., and Albrecht G. Von Arnim. eIF3 Complexes and the eIF3e Subunit in Arabidopsis Development and Translation Initiation. United States Department of Agriculture, September 2009. http://dx.doi.org/10.32747/2009.7696545.bard.

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The original working hypothesis of our proposal was that The “e” subunit of eIF3 has multiple functions from both within the nucleus and in the cytoplasm. Within this model, we further hypothesized that the “e” subunit of eIF3 functions in translation as a repressor. We proposed to test these hypotheses along the following specific aims: 1) Determine the subcellular localization of the interaction between eIF3e and other eIF3 subunits, or the COP9 signalosome. 2) Elucidate the biological significance of the varied subcellular localizations of eIF3e through generating Arabidopsis eIF3e alleles with altered subcellular localization. 3.) Purify different eIF3e complexes by tandem affinity purification (TAP). 4) Study the role of eIF3e in translational repression using both in vitro and in planta assays. eIF3 is an evolutionarily ancient and essential component of the translational apparatus in both the plant and animal kingdoms. eIF3 is the largest, and in some ways the most mysterious, of the translation factors. It is a multi-subunit protein complex that has a structural/scaffolding role in translation initiation. However, despite years of study, only recently have differential roles for eIF3 in the developmental regulation of translation been experimentally grounded. Furthermore, the roles of individual eIF3 subunits are not clear, and indeed some, such as the “e” subunit may have roles independent of translation initiation. The original three goals of the proposal were technically hampered by a finding that became evident during the course of the research – Any attempt to make transgenic plants that expressed eIF3e wt or eIF3e variants resulted in seedling lethality or seed inviability. That is, it was impossible to regenerate any transgenic plants that expressed eIF3e. We did manage to generate plants that expressed an inducible form of eIF3e. This also eventually led to lethality, but was very useful in elucidating the 4th goal of the research (Yahalom et al., 2008), where we showed, for the first time in any organism, that eIF3e has a repressory role in translation. In attempt to solve the expression problems, we also tried expression from the native promoter, and as such analyzed this promoter in transgenic plants (Epel, 2008). As such, several additional avenues were pursued. 1) We investigated protein-protein interactions of eIF3e (Paz-Aviram et al., 2008). 2) The results from goal #4 led to a novel hypothesis that the interaction of eIF3e and the CSN meets at the control of protein degradation of nascent proteins. In other words, that the block in translation seen in csn and eIF3e-overexpressing plants (Yahalom et al., 2008) leads to proteasome stress. Indeed we showed that both over expression of eIF3e and the csn mutants lead to the unfolded protein response. 3) We further investigated the role of an additional eIF3 subunit, eIF3h, in transalational regulation in the apical meristem (Zhou et al., 2009). Epel, A. (2008). Characterization of eIF3e in the model plant Arabidopsis thaliana. In Plant Sciences (Tel Aviv, Tel Aviv University). Paz-Aviram, T., Yahalom, A., and Chamovitz, D.A. (2008). Arabidopsis eIF3e interacts with subunits of the ribosome, Cop9 signalosome and proteasome. Plant Signaling and Behaviour 3, 409-411. Yahalom, A., Kim, T.H., Roy, B., Singer, R., von Arnim, A.G., and Chamovitz, D.A. (2008). Arabidopsis eIF3e is regulated by the COP9 signalosome and has an impact on development and protein translation. Plant J 53, 300-311. Zhou, F., Dunlap, J.R., and von Arnim, A.G. The translation initiation factor subunit eIF3h is .1 involved in Arabidopsis shoot apical meristem maintenance and auxin response. (submitted to Development).
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Chen, Junping, Zach Adam, and Arie Admon. The Role of FtsH11 Protease in Chloroplast Biogenesis and Maintenance at Elevated Temperatures in Model and Crop Plants. United States Department of Agriculture, May 2013. http://dx.doi.org/10.32747/2013.7699845.bard.

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specific objectives of this proposal were to: 1) determine the location, topology, and oligomerization of FtsH11 protease; 2) identify the substrate/s of FtsH11 and the downstream components involved in maintaining thermostability of chloroplasts; 3) identify new elements involved in FtsH11 protease regulatory network related to HT adaptation processes in chloroplast; 4) Study the role of FtsH11 homologs from crop species in HT tolerance. Background to the topic: HT-tolerant varieties that maintain high photosynthetic efficiency at HT, and cope better with daily and seasonal temperature fluctuations are in great need to alleviate the effect of global warming on food production. Photosynthesis is a very complex process requiring accurate coordination of many complex systems and constant adjustments to the changing environments. Proteolytic activities mediated by various proteases in chloroplast are essential part of this process and critical for maintaining normal chloroplast functions under HT. However, little is known about mechanisms that contribute to adaptation of photosynthetic processes to HT. Our study has shown that a chloroplast-targeted Arabidopsis FtsH11 protease plays an essential and specific role in maintaining thermostability of thylakoids and normal photosynthesis at moderate HT. We hypothesized that FtsH11 homologs recently identified in other plant species might have roles similarly to that of AtFtsH1. Thus, dissecting the underlying mechanisms of FtsH11 in the adaptation mechanisms in chloroplasts to HT stress and other elements involved will aid our effort to produce more agricultural products in less favorable environments. Major conclusions, solutions, achievements - Identified the chloroplast inner envelope membrane localization of FtsH11. - Revealed a specific association of FtsH11 with the a and b subunits of CPN60. - Identified the involvement of ARC6, a protein coordinates chloroplast division machineries in plants, in FtsH11 mediated HT adaptation process in chloroplast. -Reveal possible association of a polyribonucleotide nucleotidyltransferase (cpPNPase), coded by At3G03710, with FtsH11 mediated HT adaptation process in chloroplast. - Mapped 4 additional loci in FtsH11 mediated HT adaptation network in chloroplast. - Demonstrated importance of the proteolytic activity of FtsH11 for thermotolerance, in addition to the ATPase activity. - Demonstrated a conserved role of plant FtsH11 proteases in chloroplast biogenesis and in maintaining structural and functional thermostability of chloroplast at elevated temperatures. Implications, both scientific and agricultural:Three different components interacting with FtsH11 were identified during the course of this study. At present, it is not known whether these proteins are directly involved in FtsH11mediated thermotolerance network in chloroplast and/or how these elements are interrelated. Studies aiming to connect the dot among biological functions of these networks are underway in both labs. Nevertheless, in bacteria where it was first studied, FtsH functions in heat shock response by regulating transcription level of σ32, a heat chock factor regulates HSPsexpression. FtsH also involves in control of biosynthesis of membrane components and quality control of membrane proteins etc. In plants, both Arc 6 and CPN60 identified in this study are essential in chloroplast division and developments as mutation of either one impairs chloroplast division in Arabidopsis. The facts that we have found the specific association of both α and β CPN60 with FtsH11 protein biochemically, the suppression/ enhancement of ftsh11 thermosensitive phenotype by arc6 /pnp allele genetically, implicate inter-connection of these networks via FtsH11 mediated network(s) in regulating the dynamic adaptation processes of chloroplast to temperature increases at transcriptional, translational and post-translational levels. The conserved role of FtsH11 proteases in maintaining thermostability of chloroplast at HT demonstrated here provides a foundation for improving crop photosynthetic performance at high temperatures.
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