A Data-Driven Evolutionary Algorithm for Mapping Multibasin Protein Energy Landscapes
A Data-Driven Evolutionary Algorithm for Mapping Multibasin Protein Energy Landscapes
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DOI：
10.1089/cmb.2015.0107
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发表时间：
2015-09-01
影响因子：
1.7
通讯作者：
Shehu, Amarda
中科院分区：
文献类型：
--
作者：
Clausen, Rudy;Shehu, Amarda
越来越多的证据表明，许多与蛋白质病有关的蛋白质都是动态分子，在稳定和半稳定结构之间切换以调节其功能。要详细了解此类分子的结构和功能之间的关系，需要对其构象空间进行全面表征。目前，只有随机优化方法能够探索构象空间以获得相关能量表面的基于样本的表示。这些方法必须解决平衡探索（获得广阔的空间视野）和利用（深入能量表面）之间的计算资源的基本但具有挑战性的问题。我们提出了一种新颖的算法，通过采用进化计算的概念来实现有效的平衡。该算法利用野生型和蛋白质变异序列的沉积晶体结构来定义一个简化的低维搜索空间，从中快速抽取样本。多尺度技术将样本映射到所研究蛋白质的全原子能量表面的局部最小值。采用几种新颖的算法策略来避免过早收敛到特定最小值并获得可能的多盆地能量表面的广阔视野。对不同蛋白质的应用分析证明了该算法在绘制多盆地能源景观和推进多盆地蛋白质建模方面的广泛实用性。特别是，对参与蛋白质病的蛋白质野生型和变异序列的应用表明，该算法通过提供能量景观作为蛋白质序列和功能之间的中间解释联系，在理解序列突变对功能障碍的影响方面迈出了重要的第一步。
Evidence is emerging that many proteins involved in proteinopathies are dynamic molecules switching between stable and semistable structures to modulate their function. A detailed understanding of the relationship between structure and function in such molecules demands a comprehensive characterization of their conformation space. Currently, only stochastic optimization methods are capable of exploring conformation spaces to obtain sample-based representations of associated energy surfaces. These methods have to address the fundamental but challenging issue of balancing computational resources between exploration (obtaining a broad view of the space) and exploitation (going deep in the energy surface). We propose a novel algorithm that strikes an effective balance by employing concepts from evolutionary computation. The algorithm leverages deposited crystal structures of wildtype and variant sequences of a protein to define a reduced, low-dimensional search space from where to rapidly draw samples. A multiscale technique maps samples to local minima of the all-atom energy surface of a protein under investigation. Several novel algorithmic strategies are employed to avoid premature convergence to particular minima and obtain a broad view of a possibly multibasin energy surface. Analysis of applications on different proteins demonstrates the broad utility of the algorithm to map multibasin energy landscapes and advance modeling of multibasin proteins. In particular, applications on wildtype and variant sequences of proteins involved in proteinopathies demonstrate that the algorithm makes an important first step toward understanding the impact of sequence mutations on misfunction by providing the energy landscape as the intermediate explanatory link between protein sequence and function.