Yang Yuedong, Liu Haiyan
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.
J Comput Chem. 2006 Oct;27(13):1593-602. doi: 10.1002/jcc.20463.
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.
我们基于遗传算法和离散主链二面角状态模型研究了蛋白质构象采样与优化。提出了一种将遗传算法与局部最小化以及基于共享函数的小生境技术相结合的有效方法。使用两种不同类型的势能函数，即Go型势能函数和基于知识的成对势能函数，以及一个包含不同大小和二级结构组成的小蛋白质的测试集，我们证明了局部最小化和种群多样性在遗传算法蛋白质构象优化中的重要性。讨论了采样构象的一些一般性质，如它们与天然构象的相似性以及包含侧链的影响。
