An efficient hybrid of hill-climbing and genetic algorithm for 2D triangular protein structure prediction

Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, embryonic development, DNA and RNA synthesis and others. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model could give a better structure modeling and prediction for proteins with short primary amino acid sequences. In this paper it is proposed that elite-based reproduction strategy (ERS) genetic algorithm (GA) and a hybrid of hill-climbing and genetic algorithm (HHGA) for protein structure prediction on the 2D triangular lattice. It is hoped that other researchers can note the importance of this model. The simulation results of the experiments show that the elite-based reproduction strategy (ERS) genetic algorithm (GA) and hybrid hill-climbing genetic algorithm (HHGA) can successfully be applied to protein structure prediction problems.