A novel ab-initio genetic-based approach for protein folding prediction

In this paper, a model based on genetic algorithms for protein folding prediction is proposed. The most important features of the proposed approach are: i) Heuristic secondary structure information is used in the initialization of the genetic algorithm; ii) An enhanced 3D spatial representation called cube-octahedron is used, also, an expansion technique is proposed in order to reduce the computational complexity and spatial constraints; iii) Data preprocessing of geometric features to characterize the cube-octahedron using twelve basic vectors to define the nodes. Additionally, biological information (torsion angles, bond angles and secondary structure conformations) was pre-processed through an analysis of all possible combinations of the basic vectors which satisfy the biological constrains defined by the spatial representation; and iv) Hashing techniques were used to improve the computational efficiency. The pre-processed information was stored in hash tables, which are intensively used by the genetic algorithm. Some experiments were carried out to validate the proposed model obtaining very promising results.