Authors:
Trent Higgs
1
;
Bela Stantic
1
;
Tamjidul Hoque
2
and
Abdul Sattar
3
Affiliations:
1
Griffith University, Australia
;
2
Indiana University Purdue University Indianapolis (IUPUI), United States
;
3
Griffith University and NICTA Queensland Research Laboratory, Australia
Keyword(s):
Genetic algorithms, Protein structure prediction, Feature-based resampling.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Genomics and Proteomics
;
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 modelling score (TM-Score). From our analysis we can conclude that by combining
feature-based resampling with Genetic Algorithms we can cre
ate 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.
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