Feature space resampling for protein conformational search.
Feature space resampling for protein conformational search.
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DOI：
10.1002/prot.22677
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发表时间：
2010-05-01
影响因子：
2.9
通讯作者：
Baker, David
中科院分区：
文献类型：
--
作者：
Blum, Ben;Jordan, Michael I.;Baker, David
从头蛋白质结构预测需要在大量可能的构象中定位多肽链的最低能量状态。强大的方法包括构象空间退火，其中搜索逐步集中在构象空间的最有前途的地区，和遗传算法，其中迄今为止确定的最佳构象的功能进行重组。我们描述了一种新的方法，结合了这两种方法的优势。蛋白质构象被投影到一个离散的特征空间，其中包括骨干扭转角，二级结构，和β配对。对于每一个，都有一个“原生”值：在原生结构中找到的值。我们开始与大量的构象产生独立的Monte Carlo结构预测轨迹从罗塞塔。每个特征的固有值是从特征值出现的频率和包含它们的构象中的能量分布预测的。第二轮结构预测轨迹然后由预测的原生特征分布引导。我们表明，本地功能可以预测在远高于背景率，并使用预测的特征分布提高了结构预测在基准的28个蛋白质。我们的方法允许通过重组第一轮构象的天然样部分来生成成功的模型。我们的方法的优点是，来自许多不同的输入结构的功能可以同时组合，而不会产生原子冲突或其他物理上不可行的模型，被重组的功能有一个相对较高的机会是正确的。
De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are recombined. We describe a new approach that combines the strengths of these two approaches. Protein conformations are projected onto a discrete feature space which includes backbone torsion angles, secondary structure, and beta pairings. For each of these there is one “native” value: the one found in the native structure. We begin with a large number of conformations generated in independent Monte Carlo structure prediction trajectories from Rosetta. Native values for each feature are predicted from the frequencies of feature value occurrences and the energy distribution in conformations containing them. A second round of structure prediction trajectories are then guided by the predicted native feature distributions. We show that native features can be predicted at much higher than background rates, and that using the predicted feature distributions improves structure prediction in a benchmark of 28 proteins. Our approach allows generation of successful models by recombining native-like parts of first-round conformations. The advantages of our approach are that features from many different input structures can be combined simultaneously without producing atomic clashes or otherwise physically unviable models, and that the features being recombined have a relatively high chance of being correct.
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DOI：
10.1002/prot.340230319
发表时间：
1995-11-01
期刊：
PROTEINS-STRUCTURE FUNCTION AND GENETICS
影响因子：
--
作者：
PEDERSEN, JT;MOULT, J
通讯作者：
MOULT, J
影响因子：
5.6
作者：
Kim DE;Blum B;Bradley P;Baker D
通讯作者：
Baker D
影响因子：
5.8
作者：
Brunette, TJ;Brock, O
通讯作者：
Brock, O
影响因子：
2.9
作者：
Das, Rhiju;Bin Qian;Baker, David
通讯作者：
Baker, David
DOI：
10.1073/pnas.1831973100
发表时间：
2003-10-14
影响因子：
11.1
作者：
Meiler, J;Baker, D
通讯作者：
Baker, D