A conformation ensemble approach to protein residue-residue contact

IF 2.222 Q3 Biochemistry, Genetics and Molecular Biology BMC Structural Biology Pub Date : 2011-10-12 DOI:10.1186/1472-6807-11-38
Jesse Eickholt, Zheng Wang, Jianlin Cheng
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引用次数: 17

Abstract

Protein residue-residue contact prediction is important for protein model generation and model evaluation. Here we develop a conformation ensemble approach to improve residue-residue contact prediction. We collect a number of structural models stemming from a variety of methods and implementations. The various models capture slightly different conformations and contain complementary information which can be pooled together to capture recurrent, and therefore more likely, residue-residue contacts.

We applied our conformation ensemble approach to free modeling targets from both CASP8 and CASP9. Given a diverse ensemble of models, the method is able to achieve accuracies of. 48 for the top L/5 medium range contacts and. 36 for the top L/5 long range contacts for CASP8 targets (L being the target domain length). When applied to targets from CASP9, the accuracies of the top L/5 medium and long range contact predictions were. 34 and. 30 respectively.

When operating on a moderately diverse ensemble of models, the conformation ensemble approach is an effective means to identify medium and long range residue-residue contacts. An immediate benefit of the method is that when tied with a scoring scheme, it can be used to successfully rank models.

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蛋白质残基-残基接触的构象集合方法
蛋白质残基-残基接触预测是蛋白质模型生成和模型评价的重要内容。本文提出了一种改进残渣接触预测的构象集成方法。我们收集了许多来自各种方法和实现的结构模型。不同的模型捕捉到稍微不同的构象,并包含互补的信息,这些信息可以汇集在一起捕捉到循环的,因此更有可能是残基-残基接触。我们将我们的构象集成方法应用于CASP8和CASP9的自由建模目标。给定不同的模型集合,该方法能够达到的精度为。48为顶级L/5中程触点和。36为顶部L/5 CASP8靶标的远程接触(L为靶标结构域长度)。当应用于来自CASP9的目标时,前L/5中远程接触预测的准确性为。34和。分别为30。当对中等多样性的模型集合进行操作时,构象集合方法是识别中远程残馀接触的有效手段。该方法的一个直接好处是,当与评分方案结合使用时,它可以用来成功地对模型进行排名。
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来源期刊
BMC Structural Biology
BMC Structural Biology 生物-生物物理
CiteScore
3.60
自引率
0.00%
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0
期刊介绍: BMC Structural Biology is an open access, peer-reviewed journal that considers articles on investigations into the structure of biological macromolecules, including solving structures, structural and functional analyses, and computational modeling.
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