Relevant bibliographies by topics / Ramachandran plot

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Ramachandran plot'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Ramachandran plot"

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Sheik, S. S., P. Sundararajan, A. S. Z. Hussain, and K. Sekar. "Ramachandran plot on the web." Bioinformatics 18, no. 11 (November 1, 2002): 1548–49. http://dx.doi.org/10.1093/bioinformatics/18.11.1548.

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Carugo, Oliviero, and Kristina Djinović-Carugo. "A proteomic Ramachandran plot (PRplot)." Amino Acids 44, no. 2 (September 25, 2012): 781–90. http://dx.doi.org/10.1007/s00726-012-1402-z.

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K. Gopalakrishnan, G. Sowmiya, S. S. Sheik, and K. Sekar. "Ramachandran Plot on The Web (2.0)." Protein & Peptide Letters 14, no. 7 (July 1, 2007): 669–71. http://dx.doi.org/10.2174/092986607781483912.

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Gopalakrishnan, K., S. Saravanan, R. Sarani, and K. Sekar. "RPMS: Ramachandran plot for multiple structures." Journal of Applied Crystallography 41, no. 1 (January 16, 2008): 219–21. http://dx.doi.org/10.1107/s0021889807053708.

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An interactive internet computing server,RPMS(Ramachandran plot for multiple structures) has been developed to visualize the Ramachandran angles of several highly homologous protein structures in a single plot. Options are provided for users to locate the amino acid residues in various regions of the plot. To perform the above, users need to enter the Protein Data Bank (PDB) identification codes. In addition, users can upload the atomic coordinates from the local machine. A Java graphics interface has been deployed and the server has been interfaced with a locally maintained PDB anonymous FTP server, which is updated weekly. The serverRPMScan be accessed through the Bioinformatics web server at http://cluster.physics.iisc.ernet.in/rpms/.
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Iwaoka, M., M. Okada, and S. Tomoda. "Quantum Chemical Study of Ramachandran Plot." Seibutsu Butsuri 39, supplement (1999): S115. http://dx.doi.org/10.2142/biophys.39.s115_1.

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Hollingsworth, Scott A., and P. Andrew Karplus. "A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins." BioMolecular Concepts 1, no. 3-4 (October 1, 2010): 271–83. http://dx.doi.org/10.1515/bmc.2010.022.

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AbstractThe Ramachandran plot is among the most central concepts in structural biology, seen in publications and textbooks alike. However, with the increasing numbers of known protein structures and greater accuracy of ultra-high resolution protein structures, we are still learning more about the basic principles of protein structure. Here, we use high-fidelity conformational information to explore novel ways, such as geo-style and wrapped Ramachandran plots, to convey some of the basic aspects of the Ramachandran plot and of protein conformation. We point out the pressing need for a standard nomenclature for peptide conformation and propose such a nomenclature. Finally, we summarize some recent conceptual advances related to the building blocks of protein structure. The results for linear groups imply the need for substantive revisions in how the basics of protein structure are handled.
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Mannige, Ranjan V. "An exhaustive survey of regular peptide conformations using a new metric for backbone handedness (h)." PeerJ 5 (May 16, 2017): e3327. http://dx.doi.org/10.7717/peerj.3327.

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The Ramachandran plot is important to structural biology as it describes a peptide backbone in the context of its dominant degrees of freedom—the backbone dihedral angles φ and ψ (Ramachandran, Ramakrishnan & Sasisekharan, 1963). Since its introduction, the Ramachandran plot has been a crucial tool to characterize protein backbone features. However, the conformation or twist of a backbone as a function of φ and ψ has not been completely described for both cis and trans backbones. Additionally, little intuitive understanding is available about a peptide’s conformation simply from knowing the φ and ψ values of a peptide (e.g., is the regular peptide defined by φ = ψ = − 100° left-handed or right-handed?). This report provides a new metric for backbone handedness (h) based on interpreting a peptide backbone as a helix with axial displacement d and angular displacement θ, both of which are derived from a peptide backbone’s internal coordinates, especially dihedral angles φ, ψ and ω. In particular, h equals sin(θ)d∕|d|, with range [−1, 1] and negative (or positive) values indicating left(or right)-handedness. The metric h is used to characterize the handedness of every region of the Ramachandran plot for both cis (ω = 0°) and trans (ω = 180°) backbones, which provides the first exhaustive survey of twist handedness in Ramachandran (φ, ψ) space. These maps fill in the ‘dead space’ within the Ramachandran plot, which are regions that are not commonly accessed by structured proteins, but which may be accessible to intrinsically disordered proteins, short peptide fragments, and protein mimics such as peptoids. Finally, building on the work of (Zacharias & Knapp, 2013), this report presents a new plot based on d and θ that serves as a universal and intuitive alternative to the Ramachandran plot. The universality arises from the fact that the co-inhabitants of such a plot include every possible peptide backbone including cis and trans backbones. The intuitiveness arises from the fact that d and θ provide, at a glance, numerous aspects of the backbone including compactness, handedness, and planarity.
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Zhou, Alice Qinhua, Corey S. O'Hern, and Lynne Regan. "Revisiting the Ramachandran plot from a new angle." Protein Science 20, no. 7 (May 31, 2011): 1166–71. http://dx.doi.org/10.1002/pro.644.

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Porter, Lauren L., and George D. Rose. "Redrawing the Ramachandran plot after inclusion of hydrogen-bonding constraints." Proceedings of the National Academy of Sciences 108, no. 1 (December 8, 2010): 109–13. http://dx.doi.org/10.1073/pnas.1014674107.

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A protein backbone has two degrees of conformational freedom per residue, described by its φ,ψ-angles. Accordingly, the energy landscape of a blocked peptide unit can be mapped in two dimensions, as shown by Ramachandran, Sasisekharan, and Ramakrishnan almost half a century ago. With atoms approximated as hard spheres, the eponymous Ramachandran plot demonstrated that steric clashes alone eliminate ¾ of φ,ψ-space, a result that has guided all subsequent work. Here, we show that adding hydrogen-bonding constraints to these steric criteria eliminates another substantial region of φ,ψ-space for a blocked peptide; for conformers within this region, an amide hydrogen is solvent-inaccessible, depriving it of a hydrogen-bonding partner. Yet, this “forbidden” region is well populated in folded proteins, which can provide longer-range intramolecular hydrogen-bond partners for these otherwise unsatisfied polar groups. Consequently, conformational space expands under folding conditions, a paradigm-shifting realization that prompts an experimentally verifiable conjecture about likely folding pathways.
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Porter, Lauren L., and George D. Rose. "Comment on “Revisiting the Ramachandran plot from a new angle”." Protein Science 20, no. 11 (October 13, 2011): 1771–73. http://dx.doi.org/10.1002/pro.724.

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Dissertations / Theses on the topic "Ramachandran plot"

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Chakraborty, Promita. "A Computational Framework for Interacting with Physical Molecular Models of the Polypeptide Chain." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/47932.

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Although nonflexible, scaled molecular models like Pauling-Corey's and its descendants have made significant contributions in structural biology research and pedagogy, recent technical advances in 3D printing and electronics make it possible to go one step further in designing physical models of biomacromolecules: to make them conformationally dynamic. We report the design, construction, and validation of a flexible, scaled, physical model of the polypeptide chain, which accurately reproduces the bond rotational degrees-of-freedom in the peptide backbone. The coarse-grained backbone model consists of repeating amide and alpha-carbon units, connected by mechanical bonds (corresponding to phi and psi angles) that include realistic barriers to rotation that closely approximate those found at the molecular scale. Longer-range hydrogen-bonding interactions are also incorporated, allowing the chain to easily fold into stable secondary structures. This physical model can serve as the basis for linking tangible bio-macromolecular models directly to the vast array of existing computational tools to provide an enhanced and interactive human-computer interface. We have explored the boundaries of this direction at the interface of computational tools and physical models of biological macromolecules at the nano-scale. Using a CAD-biocomputational framework, we have provided a methodology to design and build physical protein models focusing on shape and dynamics. We have also developed a workflow and an interface implemented for such bio-modeling tools. This physical-digital interface paradigm, at the intersection of native state proteins (P), computational models (C) and physical models (P), provides new opportunities for building an interactive computational modeling tool for protein folding and drug design. Furthermore, this model is easily constructed with readily obtainable parts and promises to be a tremendous educational aid to the intuitive understanding of chain folding as the basis for macromolecular structure.
Ph. D.
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Chen, Yen-Ru, and 陳彥儒. "A Protein Structure Prediction Method Based on Ramachandran Plot." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/sa3566.

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碩士
國立東華大學
資訊工程學系
95
In general, the structure of a protein will be changed if its primary sequence is changed. However, not every change in sequence results in a change in structure. Properties of protein structure cannot be detected precisely by sequence alignment methods. Therefore, to establish evolutionary relationship between proteins that share no or nearly no common primary structures is helpful to the annotation and characterization of biological processes. In this thesis, we propose a protein secondary structure prediction method based on Ramachandran region. By training a known data set, we can predict the φ and ψ angles of an unknown protein. Then, according to the distribution of Ramachandran plot on φ and ψ backbone conformational angles, we obtain a secondary structure of this unknown protein. By our experimental results, through protein myoglobin 1A6M and 2FAM are very different in their primary structures, they shares 81.82% of the common structures. That is, our results show that the proposed method provides a promotive advantage on protein structure prediction.
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Kalvoda, Tadeáš. "Studium konformačního chování krátkých peptidových fragmentů metodami kvantové chemie." Master's thesis, 2020. http://www.nusl.cz/ntk/nusl-436427.

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To what extent conformational preference of short peptide sequences within proteins determine their three-dimensional structure? Large-scale quantum chemical calculations coupled with modern solvation methods represent unique set of tools to elucidate key determinants of the biomolecular structure ab initio. Full conformational sampling was performed on model systems representing short peptide fragments. The computed data reveal some of the underlying physico-chemical principles determining the spatial structure of proteins, and provide very important data for finding and tuning the optimal algorithm that may provide a full coverage of (ideally all) low-energy conformers. Keywords: Conformational space, peptide fragments, protein structure, solvation methods, Ramachandran plot, DFT-D3 methods
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Chen, Wei-Chen, and 陳維成. "Bonding, Substituent and Hydrogen Bonding Effects on Structure and Cyclization Reaction: Using Density Functional Theory to Study the Myers-Satio Reaction, the Adamantan-2-one Cycloaddition Reaction and the Potential Energy Surface of Ramachandran plot." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/92172298082254317600.

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碩士
國立清華大學
化學系
87
This thesis includes three parts: the potential energy surface corresponding to the Ramachandran plot, the 1,3-dipolar cycloaddition reactions of acetonitrile oxide with 5-substituted adamantan-2-one and its derivatives, and the structure effects on the Myers-Satio reaction. In the study of Ramachandran plot, the backbone energy of glycine was estimated using a model molecule with the B3LYP/D95++** method. The beta and beta'-sheets distribute at the ranges of energy lower than 2 kcal/mol. The alpha-helix and collagen structures are at higher energy regions of 5 ~ 7 kcal/mol. Thus, the backbone energy of most amino acids in proteins are concluded to be lower than 7 kcal/mol. The face selectivity of the 1,3-dipolar cycloaddition system was studied by the BLYP and BPW91 method with the 3-21G** basis set. Although the basis set is too small to accurately predict comparable results with experiments, the structural difference can still offer some useful information. The hyper conjugation effect exists in the adamantan-2-one and its derivatives; therefore, the four CC bonds near the C=Y (Y = O, S and CH2) groups have longer bond lengths, about 1.57 ~ 1.58 angstroms. The most important interaction that determines the face selectivity of this reaction is the hyperconjugation destruction from the reactants to the transition structures. The BPW91/6-311G** method are employed for studying the Myers-Satio reaction. The ring structure of the reactants lowers the activation energy and reduces the temperature effect on the thermodynamic data. The through bond interaction plays a significant role in the reaction 10 of a nine-membered ring compound. The reactivity of reaction 10 is similar to the Bergman reaction instead of the general Myers-Satio reaction due to the strong through bond interaction. Thus, 10 can be a reaction center of certain Bergman anti-tumor drug. A straightforward method to determine whether a reaction has the early or the late transition structure by the change of entropy was also derived. The late transition structures of Myers-Satio reactions confirm the validity of this method. The reaction centers of the Bergman anti-tumor drugs have very similar (Delta)Sa : (Delta)S ratio, 0.90 ~ 0.94 at 310.65 K, so they can almost have the same reactivity.
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Book chapters on the topic "Ramachandran plot"

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Gooch, Jan W. "Ramachandran Plot." In Encyclopedic Dictionary of Polymers, 919. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_14641.

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LASKOWSKI, ROMAN A., NICHOLAS FURNHAM, and JANET M. THORNTON. "THE RAMACHANDRAN PLOT AND PROTEIN STRUCTURE VALIDATION." In Biomolecular Forms and Functions, 62–75. WORLD SCIENTIFIC / INDIAN INST OF SCIENCE, INDIA, 2013. http://dx.doi.org/10.1142/9789814449144_0005.

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Conference papers on the topic "Ramachandran plot"

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Purushe, Shweta, Sanjay Krishna Anbalagan, and Georges Grinstein. "Development of an Interactive Ramachandran Plot in Weave." In 2011 15th International Conference Information Visualisation (IV). IEEE, 2011. http://dx.doi.org/10.1109/iv.2011.109.

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Maack, Robin Georg Claus, Christina Gillmann, and Hans Hagen. "Uncertainty-Aware Ramachandran Plots." In 2019 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2019. http://dx.doi.org/10.1109/pacificvis.2019.00034.

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Alexander-Uribe, Jonny, Julián D. Arias-Londoño, and Alexandre Perera-Lluna. "Protein Disorder Prediction using Jumping Motifs from Torsion Angles Dynamics in Ramachandran Plots." In 9th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006647900380048.

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Uribe, Jonny A., Julián D. Arias-Londoño, and Alexandre Perera-Lluna. "Protein Disorder Prediction using Information Theory Measures on the Distribution of the Dihedral Torsion Angles from Ramachandran Plots." In 8th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006140500430051.

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