Stochastic modeling of noninteracting probes in the protein structure space for construction of knowledge-based potentials for atom-atom interactions

BIOPHYSICS ◽  
2008 ◽  
Vol 53 (3) ◽  
pp. 189-194
Author(s):  
S. V. Rakhmanov ◽  
V. J. Makeev
Keyword(s):  
Protein Structure ◽  
Stochastic Modeling ◽  
Construction Of Knowledge ◽  
Structure Space ◽  
Knowledge Based ◽  
Protein Structure Space

scholarly journals Short paths in protein structure space originate in graph structure

2009 ◽  
Vol 106 (51) ◽  
pp. E137-E137 ◽  
Author(s):  
M. Zimmermann ◽  
F. Towfic ◽  
R. L. Jernigan ◽  
A. Kloczkowski
Keyword(s):  
Protein Structure ◽  
Graph Structure ◽  
Structure Space ◽  
Protein Structure Space

scholarly journals Fragnostic: walking through protein structure space

2005 ◽  
Vol 33 (Web Server) ◽  
pp. W249-W251 ◽  
Author(s):  
I. Friedberg ◽  
A. Godzik
Keyword(s):  
Protein Structure ◽  
Structure Space ◽  
Protein Structure Space

scholarly journals Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space

10.29007/j5p9 ◽  
2019 ◽  
Author(s):  
Ahmed Bin Zaman ◽  
Amarda Shehu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structures ◽  
Free Protein ◽  
Structure Space ◽  
Template Free ◽  
Low Dimensional ◽  
Protein Structure Space

A central challenge in template-free protein structure prediction is controlling the quality of computed tertiary structures also known as decoys. Given the size, dimensionality, and inherent characteristics of the protein structure space, this is non-trivial. The current mechanism employed by decoy generation algorithms relies on generating as many decoys as can be afforded. This is impractical and uninformed by any metrics of interest on a decoy dataset. In this paper, we propose to equip a decoy generation algorithm with an evolving map of the protein structure space. The map utilizes low-dimensional representations of protein structure and serves as a memory whose granularity can be controlled. Evaluations on diverse target sequences show that drastic reductions in storage do not sacrifice decoy quality, indicating the promise of the proposed mechanism for decoy generation algorithms in template-free protein structure prediction.

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