Church B W, Shalloway D
Biophysics Program, Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA.
Proc Natl Acad Sci U S A. 2001 May 22;98(11):6098-103. doi: 10.1073/pnas.101030498. Epub 2001 May 8.
The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a beta-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.
势能景观的层次特性已被用于深入了解蛋白质集合的热力学和动力学特性。利用这些特性来指导对热力学稳定宏观状态的计算搜索也是有可能的，即计算蛋白质折叠。为此，我们开发了一种自上而下的搜索程序，其中构象空间根据景观有效能量障碍的内在层次结构进行递归剖析。该程序生成一棵倒置的树，类似于局部极小值聚类方法生成的不连通图，但在选择要进行计算探索的树部分的方式上有根本区别。一个关键要素是一种分支选择算法，该算法利用景观的统计预测特性来引导搜索沿着最有可能导致物理相关宏观状态的树枝向下进行。以形成β-发夹的肽的计算折叠为例，我们表明这种预测特性确实存在，并且可用于通过自由能全局最小化进行结构预测。
