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高度精确的基于序列的蛋白质中氨基酸残基半球暴露预测。

Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins.

机构信息

Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.

Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia, Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia, Medical Research Center (MRC), Department of Psychiatry, University of Iowa, Iowa City, USA.

出版信息

Bioinformatics. 2016 Mar 15;32(6):843-9. doi: 10.1093/bioinformatics/btv665. Epub 2015 Nov 14.

Abstract

MOTIVATION

Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ.

RESULTS

This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction.

AVAILABILITY AND IMPLEMENTATION

The method is available at http://sparks-lab.org

CONTACT

yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

氨基酸残基在蛋白质中的溶剂暴露在理解和预测蛋白质结构、功能和相互作用方面起着重要作用。溶剂暴露可以通过几种方法来描述,包括溶剂可及表面积 (ASA)、残基深度 (RD) 和接触数 (CN)。最近,根据 Cα-Cβ(HSEβ)向量或相邻 Cα-Cα 向量(HSEα)定义的上半球和下半球,引入了一种定向依赖的接触数,称为半球暴露 (HSE)。从蛋白质结构中计算出的 HSEα 在许多应用中被发现比 ASA、CN 和 RD 更好地描述溶剂暴露。因此,需要一种基于序列的预测方法,因为大多数蛋白质没有实验确定的结构。据我们所知,目前还没有预测 HSEα 的方法,只有一种预测 HSEβ 的方法。

结果

本研究开发了一种预测 HSEα 和 HSEβ 的新方法 (SPIDER-HSE),该方法在 10 倍交叉验证和两个独立测试中均具有一致的性能。对于 1199 个蛋白质的独立测试集,预测的 HSEβ 与实测 HSEβ 之间的相关系数(上半球为 0.73,下半球为 0.69,接触数为 0.76)显著高于现有方法。此外,与预测的 HSEβ(0.37)和 ASA(0.43)相比,预测的 HSEα 与残基突变体稳定性变化的相关性更高(0.46)。这些结果以及其基于 Cα-原子的简单计算,突出了预测的 HSEα 在蛋白质结构预测和细化以及功能预测方面的潜在有用性。

可用性和实现

该方法可在 http://sparks-lab.org 上获得。

联系方式

yuedong.yang@griffith.edu.auyaoqi.zhou@griffith.edu.au

补充信息

补充数据可在 Bioinformatics 在线获得。

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