Applying Feature-Based Resampling to Protein Structure Prediction
Author(s)
Higgs, T
Stantic, B
Md. Hoque, T
Sattar, A
Year published
2012
Metadata
Show full item recordAbstract
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, ...
View more >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.
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View more >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.
View less >
Conference Title
4th International Conference on Bioinformatics and Computational Biology 2012, BICoB 2012
Publisher URI
Subject
Artificial intelligence not elsewhere classified