Combining physicochemical and evolutionary information for protein contact prediction.

Combining physicochemical and evolutionary information for protein contact prediction.
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结合蛋白质接触预测的物理化学和进化信息。

DOI:
10.1371/journal.pone.0108438
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发表时间:
2014
期刊:
影响因子:
3.7
通讯作者:
Brock O
Brock O
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Schneider M;Brock O

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我们介绍了一种新的接触预测方法,实现高预测精度相结合的进化和物理化学信息的本地接触。我们从多序列比对中获得进化信息,从预测的从头开始蛋白质结构中获得理化信息。这些结构代表能量景观中的低能态,从而捕获能量函数中编码的物理化学信息。这种低能结构很可能包含原生接触,即使它们的整体折叠不是原生的。为了区分这些结构中的本地和非本地接触,我们开发了一个基于图形的结构上下文的接触表示。然后,我们使用这种表示来训练支持向量机分类器,以识别在其他非原生结构中最有可能的原生接触。由此产生的接触预测是高度准确的。由于结合了两个信息来源-进化和物理化学-我们保持预测的准确性,即使只有少数同源序列存在。我们表明,预测的接触有助于提高从头计算结构预测。可在http://compbio.robotics.tu-berlin.de/epc-map/上获得网络服务。
We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information—evolutionary and physicochemical—we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.
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