A New Prediction Protein Structure Method Based on Genetic Algorithm and Coarse-Grained Protein Model

2009 ◽  
Author(s):  
Cai-Yun Wang ◽  
Hao-Dong Zhu ◽  
Le-Cai Cai
Keyword(s):  
Genetic Algorithm ◽  
Protein Structure ◽  
Coarse Grained ◽  
Protein Model

scholarly journals Reoptimized UNRES Potential for Protein Model Quality Assessment

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 601 ◽  
Author(s):  
Eshel Faraggi ◽  
Pawel Krupa ◽  
Magdalena Mozolewska ◽  
Adam Liwo ◽  
Andrzej Kloczkowski
Keyword(s):  
Protein Structure ◽  
Coarse Grained ◽  
Poor Correlation ◽  
Native Structure ◽  
Feed Forward Neural Network ◽  
Model Quality ◽  
Protein Model ◽  
Protein Model Quality Assessment ◽  
Dynamics Simulations ◽  
Degree Of Similarity

Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model’s Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. The reoptimized version of UNRES (OUNRES) is available at http://mamiris.com and http://www.unres.pl.

Novel use of a genetic algorithm for protein structure prediction: Searching template and sequence alignment space

2003 ◽  
Vol 53 (S6) ◽  
pp. 424-429 ◽  
Author(s):  
Bruno Contreras-Moreira ◽  
Paul W. Fitzjohn ◽  
Marc Offman ◽  
Graham R. Smith ◽  
Paul A. Bates
Keyword(s):  
Genetic Algorithm ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Sequence Alignment ◽  
Structure Prediction

scholarly journals Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm

2019 ◽  
Vol 79 ◽  
pp. 6-15 ◽  
Author(s):  
Md. Lisul Islam ◽  
Swakkhar Shatabda ◽  
Mahmood A. Rashid ◽  
M.G.M. Khan ◽  
M. Sohel Rahman
Keyword(s):  
Genetic Algorithm ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Nmr Data

scholarly journals ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

2019 ◽  
Vol 47 (W1) ◽  
pp. W471-W476 ◽  
Author(s):  
Rasim Murat Aydınkal ◽  
Onur Serçinoğlu ◽  
Pemra Ozbek
Keyword(s):  
Protein Structure ◽  
Normal Mode Analysis ◽  
Shortest Paths ◽  
Protein Structures ◽  
Interaction Strength ◽  
Biological Information ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Residue Interaction ◽  
Natural Variant

AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

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