Guiding conformation space search with an all-atom energy potential.

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-based search aggregates information in the model as it progresses, and in turn uses this information to guide exploration towards regions most likely to contain a near-optimal minimum. We validate our method by predicting the structure of 32 proteins, ranging in length from 49 to 213 amino acids. Our results demonstrate that model-based search is more effective at finding low-energy conformations in high-dimensional conformation spaces than existing search methods. The reduction in energy translates into structure predictions of increased accuracy.

蛋白质结构预测的最大障碍是构象空间搜索的不足。构象空间太大，能量景观太崎岖，现有的搜索方法无法始终找到接近最优的最小值。为了缓解这个问题，我们提出了基于模型的搜索，一种新的构象空间搜索方法。基于模型的搜索利用在搜索过程中获得的高度准确的信息来构建能量景观的近似、部分模型。基于模型的搜索在进展过程中聚集模型中的信息，并反过来利用这些信息引导探索朝着最有可能包含接近最优最小值的区域进行。我们通过预测32种蛋白质的结构来验证我们的方法，这些蛋白质的长度从49到213个氨基酸不等。我们的结果表明，基于模型的搜索在高维构象空间中寻找低能构象比现有的搜索方法更有效。能量的降低转化为更高准确性的结构预测。