| Autor: |
Yuedong Yang, Haiyan Liu |
| Předmět: |
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| Zdroj: |
Journal of Computational Chemistry; 2006, Vol. 27 Issue 13, p1593-1602, 10p, 1 Chart, 4 Graphs |
| Abstrakt: |
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. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1593–1602, 2006 [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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