A parallel genetic algorithm for protein folding prediction using the 3D-HP Side Chain model

This work presents a methodology for the application of a parallel genetic algorithm (PGA) to the problem of protein folding prediction, using the 3DHP-Side Chain model. This model is more realistic than the usual 3DHP model but, on the other hand, it is has a higher degree of complexity. Specific encoding and fitness function were proposed for this model, and running parameters were experimentally set for the standard master-slave PGA. The system was tested with a benchmark of synthetic sequences, obtaining good results. An analysis of performance of the parallel implementation was done, compared with the sequential version. Overall results suggest that the approach is efficient and promising.

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