Hydrophobic-Polar Model Structure Prediction with Binary-Coded Artificial Plant Optimization Algorithm

Hydrophobic-Polar model (HP model) is simplest grid model in the protein structure prediction problem. In this model, the amino acids are divided into hydrophobic amino acids and hydrophilic amino acids, and the angles of connecting two residuals are constant. Recently, a new algorithm inspired by plant growth process is proposed and is called artificial plant optimization algorithm (APOA). Photosynthesis operator, phototropism operator and apical dominance operator are designed. However, there are few applications. In this paper, the binary-coded version is designed and applied to solve HP model structure prediction problem. To test the performance, four standard protein HP sequences with different length are employed to compare, simulation results show the validity.