The Peptide-Binding Domain of the Chaperone Protein Hsc70 Has an Unusual Secondary Structure Topology

Biochemistry ◽  
1995 ◽  
Vol 34 (19) ◽  
pp. 6261-6266 ◽  
Author(s):  
Robert C. Morshauser ◽  
Hong Wang ◽  
Gregory C. Flynn ◽  
Erik R. P. Zuiderweg
Keyword(s):  
Secondary Structure ◽  
Peptide Binding ◽  
Binding Domain ◽  
Structure Topology ◽  
Chaperone Protein ◽  
Secondary Structure Topology

IMPROVED EFFICIENCY IN CRYO-EM SECONDARY STRUCTURE TOPOLOGY DETERMINATION FROM INACCURATE DATA

2012 ◽  
Vol 10 (03) ◽  
pp. 1242006 ◽  
Author(s):  
ABHISHEK BISWAS ◽  
DONG SI ◽  
KAMAL AL NASR ◽  
DESH RANJAN ◽  
MOHAMMAD ZUBAIR ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Secondary Structures ◽  
Exact Algorithms ◽  
Optimal Topology ◽  
Sources Of Information ◽  
Dynamic Graph ◽  
Structure Topology ◽  
Density Map ◽  
Secondary Structure Topology ◽  
Protein Density

The determination of the secondary structure topology is a critical step in deriving the atomic structure from the protein density map obtained from electron cryo-microscopy technique. This step often relies on the matching of two sources of information. One source comes from the secondary structures detected from the protein density map at the medium resolution, such as 5–10 Å. The other source comes from the predicted secondary structures from the amino acid sequence. Due to the inaccuracy in either source of information, a pool of possible secondary structure positions needs to be sampled. This paper studies the question, that is, how to reduce the computation of the mapping when the inaccuracy of the secondary structure predictions is considered. We present a method that combines the concept of dynamic graph with our previous work of using constrained shortest path to identify the topology of the secondary structures. We show a reduction of 34.55% of run-time as comparison to the naïve way of handling the inaccuracies. We also show an improved accuracy when the potential secondary structure errors are explicitly sampled verses the use of one consensus prediction. Our framework demonstrated the potential of developing computationally effective exact algorithms to identify the optimal topology of the secondary structures when the inaccuracy of the predicted data is considered.

2D-RNA-coupling numbers: A new computational chemistry approach to link secondary structure topology with biological function

2007 ◽  
Vol 28 (6) ◽  
pp. 1049-1056 ◽  
Author(s):  
Humberto González-díaz ◽  
Guillermín Agüero-chapin ◽  
Javier Varona ◽  
Reinaldo Molina ◽  
Giovanna Delogu ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Computational Chemistry ◽  
Biological Function ◽  
Structure Topology ◽  
Secondary Structure Topology

scholarly journals New functionality of RNAComposer: application to shape the axis of miR160 precursor structure

2017 ◽  
Vol 63 (4) ◽  
Author(s):  
Maciej Antczak ◽  
Mariusz Popenda ◽  
Tomasz Zok ◽  
Joanna Sarzynska ◽  
Tomasz Ratajczak ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Web Application ◽  
3D Model ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Structure Topology ◽  
Precursor Structure ◽  
Secondary Structure Topology ◽  
Main Components

RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application. In this paper, we demonstrate how the latest additions to the system allow the user to significantly affect the process of 3D model composition on several computational levels. Although in general our method is based on the knowledge of secondary structure topology, currently RNAComposer offers a choice of six incorporated programs for secondary structure prediction. It allows also to apply conditional search in the database of 3D structure elements and introduce user-provided elements into the final 3D model. This new functionality contributes to a significant improvement of the predicted 3D model reliability and it facilitates better model adjustment to the experimental data. This is exemplified based on RNAComposer application for modelling of the 3D structures of precursors of miR160 family members.

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