Paper Info
Title
Benefits of Genetic Algorithm Feature-based Resampling for Protein Structure Prediction.
Abstract
Protein structure prediction (PSP) is an important task as the three-dimensional structure of a protein dictates what function it performs. PSP can be modelled on computers by searching for the global free energy minimum based on Afinsen's 'Thermodynamic Hypothesis'. To explore this free energy landscape Monte Carlo (MC) based search algorithms have been heavily utilised in the literature. However, evolutionary search approaches, like Genetic Algorithms (GA), have shown a lot of potential in low resolution models to produce more accurate predictions. In this paper we have evaluated a GA feature-based resampling approach, which uses a heavy-atom based model, by selecting 17 random CASP 8 sequences and evaluating it against two different MC approaches. Our results indicate that our GA improves both its root mean square deviation (RMSD) and template modeling score (TM-Score). From our analysis we can conclude that by combining feature-based resampling with Genetic Algorithms we can create structures with more native-like features due to the use of crossover and mutation operators, which is supported by the low RMSD values we obtained.
Year
DOI
Venue
2012
10.5220/0003770801880194
BIOINFORMATICS: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS
Keywords
Field
DocType
Genetic algorithm,Protein structure prediction,Feature-based resampling
Protein structure prediction,Informatics,Data mining,Intelligent decision support system,Computer science,Artificial intelligence,Feature based,Bioinformatics,Resampling,Machine learning,Genetic algorithm
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Trent Higgs1121.80
Bela Stantic219838.54
Tamjidul Hoque31148.61
abdul sattar41389185.70