NON-GLYCINE RESIDUES IN THE DISALLOWED REGIONS OF THE RAMACHANDRAN MAP AND THEIR NEIGHBORHOOD PREFERENCES

2013 ◽  
pp. 95-106 ◽  
Author(s):  
CHANDA R. BHAT ◽  
MOITRAYEE BHATTACHARYYA ◽  
SARASWATHI VISHVESHWARA
Keyword(s):  
Ramachandran Map ◽  
Neighborhood Preferences

scholarly journals Care and safety in neighborhood preferences for vacant lot greenspace in legacy cities

2021 ◽  
Vol 214 ◽  
pp. 104156
Author(s):  
Joan Iverson Nassauer ◽  
Noah J. Webster ◽  
Natalie Sampson ◽  
Jiayang Li
Keyword(s):  
Neighborhood Preferences ◽  
Legacy Cities

Synthesizing neighborhood preferences for automated vehicles

2020 ◽  
Vol 120 ◽  
pp. 102774
Author(s):  
Wenwen Zhang ◽  
Kaidi Wang ◽  
Sicheng Wang ◽  
Zhiqiu Jiang ◽  
Andrew Mondschein ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Neighborhood Preferences

Conformational attractors on the Ramachandran map

1999 ◽  
Vol 55 (2) ◽  
pp. 506-517 ◽  
Author(s):  
Dirk Walther ◽  
Fred E. Cohen
Keyword(s):  
Protein Structures ◽  
Structural Data ◽  
Random Coil ◽  
Helical Conformation ◽  
Structure Quality ◽  
Frequency Distributions ◽  
Ramachandran Map ◽  
Structural Types ◽  
Apparent Correlation ◽  
Independent Parameters

Frequency distributions of protein backbone dihedral angles φ and ψ have been analyzed systematically for their apparent correlation with various crystallographic parameters, including the resolution at which the protein structures had been determined, the R factor and the free R factor, and the results have been displayed in novel differential Ramachandran maps. With improved sensitivity compared with conventionally derived heuristic Ramachandran maps, such differential maps automatically reveal conformational `attractors' to which φ/ψ distributions converge as the crystallographic resolution improves, as well as conformations tied specifically to low-resolution structures. In particular, backbone angular combinations associated with residues in α-helical conformation show a pronounced consolidation with substantially narrowed φ/ψ distributions at higher (better) resolution. Convergence to distinct conformational attractors was also observed for all other secondary-structural types and random-coil conformations. Similar resolution-dependent φ/ψ evolutions were obtained for different crystallographic refinement packages, documenting the absence of any significant artificial biases in the refinement programs investigated here. A comparison of differential Ramachandran maps derived for the R factor and the free R factor as independent parameters proved the better suitability of the free R factor for structure-quality assessment. The resolution-based differential Ramachandran map is available as a reference for comparison with actual protein structural data under WebMol, a Java-based structure viewing and analysis program (http://www.cmpharm.ucsf.edu/cgi-bin/webmol.pl).

scholarly journals Chandrasekharan Ramakrishnan (1939–2019): The student behind the Ramachandran map

2019 ◽  
Vol 28 (11) ◽  
pp. 1920-1922
Author(s):  
Narayanaswamy Srinivasan
Keyword(s):  
Ramachandran Map

On residues in the disallowed region of the Ramachandran map

Biopolymers ◽  
2002 ◽  
Vol 63 (3) ◽  
pp. 195-206 ◽  
Author(s):  
Debnath Pal ◽  
Pinak Chakrabarti
Keyword(s):  
Ramachandran Map

scholarly journals Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

2020 ◽  
Vol 16 (1) ◽  
pp. e1007516
Author(s):  
Jennifer Hannig ◽  
Hendrik Schäfer ◽  
Jörg Ackermann ◽  
Marie Hebel ◽  
Tim Schäfer ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Tumor Cells ◽  
Bioinformatics Analysis ◽  
Neighborhood Preferences ◽  
Whole Slide Images

THE RAMACHANDRAN MAP OF MORE THAN 6,500 PERFECT POLYPEPTIDE CHAINS

2007 ◽  
Vol 02 (03n04) ◽  
pp. 267-271
Author(s):  
ZOLTÁN SZABADKA ◽  
RAFAEL ÖRDÖG ◽  
VINCE GROLMUSZ
Keyword(s):  
Protein Data Bank ◽  
Structural Information ◽  
Data Bank ◽  
Ramachandran Map ◽  
Tenfold Increase ◽  
Automated Processing ◽  
Data Points ◽  
Α Helix ◽  
The Right ◽  
Polypeptide Chains

The Protein Data Bank (PDB) is the most important depository of protein structural information, containing more than 45,000 deposited entries today. Because of its inhomogeneous structure, its fully automated processing is almost impossible. In a previous work, we cleaned and re-structured the entries in the Protein Data Bank, and from the result we have built the RS-PDB database. Using the RS-PDB database, we draw a Ramachandran-plot from 6,593 "perfect" polypeptide chains found in the PDB, containing 1,192,689 residues. This is a more than tenfold increase in the size of data analyzed before this work. The density of the data points makes it possible to draw a logarithmic heat map enhanced Ramachandran map, showing the fine inner structure of the right-handed α-helix region.

Neighborhood Evaluation in Residential Mobility

1979 ◽  
Vol 11 (4) ◽  
pp. 393-401 ◽  
Author(s):  
M T Cadwallader
Keyword(s):  
Residential Mobility ◽  
Relative Importance ◽  
Neighborhood Preferences ◽  
Evaluative Process ◽  
Context Specific

The evaluative dimensions used to rate neighborhoods, within the context of residential mobility, are investigated. It is shown that different sets of dimensions are used to evaluate different neighborhoods, although similar dimensions tend to be used for similar neighborhoods. There is also evidence that the relative importance of the neighborhood attributes, in terms of determining neighborhood preferences, are not invariant across different neighborhoods. Both these findings suggest that the evaluative process may be largely context specific.

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