Analysis of Toy Model for Protein Folding Based on Particle Swarm Optimization Algorithm

• Published in: Advances in Natural Computation pp. 636 - 645
• Main Authors: Liu, Juan, Wang, Longhui, He, Lianlian, Shi, Feng
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Apply Particle Swarm Optimization; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Maximal Iteration Step; Predict Protein Structure; Real Protein; Real Protein Structure; Evolutionary algorithm; Swarm intelligence; Neural network; Molecular biology; Computational complexity; Modeling; Protein structure; Protein; Search algorithm; Toy
• ISBN: 9783540283201; 354028320X; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539902_78

One of the main problems of computational approaches to protein structure prediction is the computational complexity. Many researches use simplified models to represent protein structure. Toy model is one of the simplification models. Finding the ground state is critical to the toy model of protein. This paper applies Particle Swarm Optimization (PSO) Algorithm to search the ground state of toy model for protein folding, and performs experiments both on artificial data and real protein data to evaluate the PSO-based method. The results show that on one hand, the PSO method is feasible and effective to search for ground state of toy model; on the other hand, toy model just can simulate real protein to some extent, and need further improvements.