IEEE/ACM Trans Comput Biol Bioinform. 2018 May-Jun;15(3):719-731. doi: 10.1109/TCBB.2016.2628745. Epub 2016 Nov 15.
Stochastic search is often the only viable option to address complex optimization problems. Recently, evolutionary algorithms have been shown to handle challenging continuous optimization problems related to protein structure modeling. Building on recent work in our laboratories, we propose an evolutionary algorithm for efficiently mapping the multi-basin energy landscapes of dynamic proteins that switch between thermodynamically stable or semi-stable structural states to regulate their biological activity in the cell. The proposed algorithm balances computational resources between exploration and exploitation of the nonlinear, multimodal landscapes that characterize multi-state proteins via a novel combination of global and local search to generate a dynamically-updated, information-rich map of a protein's energy landscape. This new mapping-oriented EA is applied to several dynamic proteins and their disease-implicated variants to illustrate its ability to map complex energy landscapes in a computationally feasible manner. We further show that, given the availability of such maps, comparison between the maps of wildtype and variants of a protein allows for the formulation of a structural and thermodynamic basis for the impact of sequence mutations on dysfunction that may prove useful in guiding further wet-laboratory investigations of dysfunction and molecular interventions.
随机搜索通常是解决复杂优化问题的唯一可行选择。最近，进化算法已被证明能够处理与蛋白质结构建模相关的具有挑战性的连续优化问题。基于我们实验室的最新工作，我们提出了一种进化算法，用于有效地映射动态蛋白质的多盆地能量景观，这些蛋白质在热力学稳定或半稳定结构状态之间切换，以调节其在细胞中的生物活性。所提出的算法通过全局搜索和局部搜索的新组合，在非线性、多模态景观之间平衡计算资源，这些景观特征是多态蛋白质，以生成蛋白质能量景观的动态更新、信息丰富的图谱。这种新的面向映射的 EA 被应用于几种动态蛋白质及其涉及疾病的变体，以说明其以计算上可行的方式映射复杂能量景观的能力。我们进一步表明，鉴于这些图谱的可用性，野生型和蛋白质变体的图谱之间的比较可以为序列突变对功能障碍的影响提供结构和热力学基础，这可能有助于指导进一步的功能障碍和分子干预的湿实验室研究。
