BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION

Trent Higgs, Bela Stantic, Tamjidul Hoque, Abdul Sattar

2012

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 modelling 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.

Paper Citation

in Harvard Style

Higgs T., Stantic B., Hoque T. and Sattar A. (2012). BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 188-194. DOI: 10.5220/0003770801880194

in Bibtex Style

@conference{bioinformatics12,
author={Trent Higgs and Bela Stantic and Tamjidul Hoque and Abdul Sattar},
title={BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},
year={2012},
pages={188-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003770801880194},
isbn={978-989-8425-90-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
TI - BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION
SN - 978-989-8425-90-4
AU - Higgs T.
AU - Stantic B.
AU - Hoque T.
AU - Sattar A.
PY - 2012
SP - 188
EP - 194
DO - 10.5220/0003770801880194