A guided genetic algorithm for protein folding prediction using 3D hydrophobic-hydrophilic model

In this paper, a Guided Genetic Algorithm (GGA) has been presented for protein folding prediction (PFP) using 3D Hydrophobic-Hydrophilic (HP) model. Effective strategies have been formulated utilizing the core formation of the globular protein, which provides the guideline for the Genetic Algorithm (GA) while predicting protein folding. Building blocks containing Hydrophobic (H) - Hydrophilic (P or Polar) covalent bond are utilized such a way that it helps form a core that maximizes the fitness. A series of operators are developed including Diagonal Move and Tilt Move to assist in implementing the building blocks in three-dimensional space. The GGA outperformed Unger's GA in 3D HP model. The overall strategy incorporates a swing function that provides a mechanism to enable the GGA to test more potential solutions and also prevent it from developing a schema that may cause it to become trapped in local minima. Further, it helps the guidelines remain non-rigid. GGA provides improved and robust performance for PFP.