遗传算法中的交替进化压力有助于蛋白质模型选择。
Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection.
作者信息
Offman Marc N, Tournier Alexander L, Bates Paul A
机构信息
Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincoln's Inn Fields Laboratories, London, WC2A 3PX, UK.
出版信息
BMC Struct Biol. 2008 Aug 1;8:34. doi: 10.1186/1472-6807-8-34.
BACKGROUND
Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.
RESULTS
In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed.
CONCLUSION
This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA.
背景
自动蛋白质建模流程正变得越来越精确；这与所有相关组件之间日益复杂的相互作用相伴而生。然而，在模板选择、优化和蛋白质模型选择方面仍有潜在的改进空间。
结果
在自动建模流程的背景下，我们分别分析了每个步骤，揭示了几个非直观的趋势，并探索了一种使用遗传算法（GA）进行蛋白质构象采样的新策略。我们应用了交替进化压力（AEP）的概念，即在GA运行过程中的中间轮次允许模型群体不受限制地线性增长。
结论
这种方法通过允许模型克服局部能量障碍来提高GA的整体性能。AEP在所有目标的40%中实现了最佳模型的选择；相比之下，普通GA的这一比例为25%。
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