scholarly journals ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yassine Ghouzam ◽  
Guillaume Postic ◽  
Pierre-Edouard Guerin ◽  
Alexandre G. de Brevern ◽  
Jean-Christophe Gelly
Keyword(s):  
Structure Prediction ◽  
Web Server ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition

scholarly journals From local structure to a global framework: recognition of protein folds

2014 ◽  
Vol 11 (95) ◽  
pp. 20131147 ◽  
Author(s):  
Agnel Praveen Joseph ◽  
Alexandre G. de Brevern
Keyword(s):  
Local Structure ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Fold Recognition ◽  
Protein Fold ◽  
Major Area ◽  
Huge Amount ◽  
Protein Fold Recognition ◽  
Structural Environment

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

scholarly journals DescFold: A web server for protein fold recognition

2009 ◽  
Vol 10 (1) ◽  
pp. 416 ◽  
Author(s):  
Ren-Xiang Yan ◽  
Jing-Na Si ◽  
Chuan Wang ◽  
Ziding Zhang
Keyword(s):  
Web Server ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm

2020 ◽  
Author(s):  
Jiangyi Shao ◽  
Bin Liu
Keyword(s):  
Structure Prediction ◽  
Transitive Closure ◽  
Source Code ◽  
Fold Recognition ◽  
Protein Fold ◽  
Data Set ◽  
Pagerank Algorithm ◽  
Protein Fold Recognition ◽  
Benchmark Data ◽  
The Relationship

Abstract As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download

scholarly journals Protein fold recognition using geometric kernel data fusion

2014 ◽  
Vol 30 (13) ◽  
pp. 1850-1857 ◽  
Author(s):  
Pooya Zakeri ◽  
Ben Jeuris ◽  
Raf Vandebril ◽  
Yves Moreau
Keyword(s):  
Data Fusion ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition
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