Bradley Philip, Baker David
University of Washington, Seattle, Washington 98195, USA.
Proteins. 2006 Dec 1;65(4):922-9. doi: 10.1002/prot.21133.
Proteins with complex, nonlocal beta-sheets are challenging for de novo structure prediction, due in part to the difficulty of efficiently sampling long-range strand pairings. We present a new, multilevel approach to beta-sheet structure prediction that circumvents this difficulty by reformulating structure generation in terms of a folding tree. Nonlocal connections in this tree allow us to explicitly sample alternative beta-strand pairings while simultaneously exploring local conformational space using backbone torsion-space moves. An iterative, energy-biased resampling strategy is used to explore the space of beta-strand pairings; we expect that such a strategy will be generally useful for searching large conformational spaces with a high degree of combinatorial complexity.
具有复杂非局部β折叠的蛋白质对于从头结构预测具有挑战性，部分原因在于难以有效地对长程链配对进行采样。我们提出了一种新的多层次β折叠结构预测方法，该方法通过根据折叠树重新构建结构生成来规避这一困难。此树中的非局部连接使我们能够明确地对替代β链配对进行采样，同时使用主链扭转空间移动来探索局部构象空间。一种迭代的、能量偏置的重采样策略用于探索β链配对空间；我们期望这种策略对于搜索具有高度组合复杂性的大构象空间通常是有用的。
