Protein structure prediction (PSP) is an important task as the three-dimensiol 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 'Thermodymic Hypothesis'. To explore this free energy landscape Monte Carlo (MC) based search algorithms have been heavily utilised in the literature. However, evolutiory 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 alysis we can conclude that by combining feature-based resampling with Genetic Algorithms we can create structures with more tive-like features due to the use of crossover and mutation operators, which is supported by the low RMSD values we obtained.

Presented at Conferences

  • 4th International Conference on Bioinformatics and Computational Biology (BICOB) (2012)

    Las Vegas, Nevada, USA