Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space.
We have investigated protein conformation sampling and optimization based on the genetic algorithm and discrete main chain dihedral state model. An efficient approach combining the genetic algorithm with local minimization and with a niche technique based on the sharing function is proposed. Using two different types of potential energy functions, a Go-type potential function and a knowledge-based pairwise potential energy function, and a test set containing small proteins of varying sizes and secondary structure compositions, we demonstrated the importance of local minimization and population diversity in protein conformation optimization with genetic algorithms. Some general properties of the sampled conformations such as their native-likeness and the influences of including side-chains are discussed.
Yang
Yuedong
Y
Hefei National Laboratory for Physical Sciences, Key Laboratory of Structural Biology, School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
Liu
Haiyan
H
D000465:Algorithms; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D011506:Proteins