Quasiphysical And Anthropopathic Algorithm For Protein Structure Prediction

Protein structure prediction problem is to predict the dimensional folding configurations from its amino acid sequence. Being a kind of important biological macromolecules, proteins undertake the majority of life activities. The ultimate purpose of studying proteins is to modify natural proteins or to design artificial proteins in order to meet the requirement of humans, In order to solve this difficult problem, in the course of studying, people have purposed many simplied models. Even so, its corresponding protein structure prediction algorithms in the problem still have NP degree of difficulty. Therefore, seeking to effective mathematics model and high-efficient and feasible algorithm has important theory value and actual meaning.The quasi-human method is learning from human beings and their rich social experiences for wisdom to solve problem. The quasi-physical method makes the original problem an optimization problem in mathematics. There is often the possibility of going to a local minimum of object function when solving the optimization problem mathematically. As for how to jump out of the trap of local minimum so that the calculation can head for a region with better prospects, the quasi-physical method is helpless. However, the quasi-human method can give us good strategies for jumping out of a trap of local minimum with the help of human beings' behavior and experiences.According to the 3D Euclidean space model for protein structure prediction which is proposed in the international. A related physical model is found. At last, a related quasiphysical and anthropopathic algorithm is formulated.