从不寻常的氨基酸中心距离预测球形蛋白中功能重要的残基

IF 2.222 Q3 Biochemistry, Genetics and Molecular Biology
Marek Kochańczyk
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引用次数: 6

摘要

性能良好的自动蛋白质功能识别方法通常包括几种互补技术。除了构建更好的共识外,它们的预测能力还可以通过添加或改进探索蛋白质正交特征的独立模块来提高。在这项工作中,我们展示了如何利用全局原子分布的探索来指示功能重要的残基。使用一组精心挑选的球形蛋白质,我们参数化连续概率密度函数,描述单个蛋白质原子的首选中心距离。使用径向密度函数的混合模型估计相对首选埋葬取决于所考虑的蛋白质的氨基酸组成。以信息论的方式评估了原子异常位置的非预期性,并直接用于关键氨基酸的鉴定。在验证研究中,我们通过搜索与配体相互作用的结合位点,测试了基于我们的方法构建的工具(称为SurpResi)的能力。该工具显示多个候选地点的成功率可与几种几何方法相媲美。我们还表明,非预期性是参与蛋白质-蛋白质相互作用的区域的特性,因此可以用于蛋白质对接预测的排名。在这项工作中实现的计算方法可以通过http://www.bioinformatics.org/surpresi.Probabilistic的Web界面免费获得,对球形蛋白质中原子中心距离的分析能够捕获由其侧链的不同大小、电荷和疏水特性引起的氨基酸的不同取向偏好。当理想的空间偏好可以从蛋白质的唯一氨基酸组成推断出来时,位于疏水不利环境中的残基可以很容易地检测到。这些残基通常直接参与结合配体或与其他蛋白质的接合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction of functionally important residues in globular proteins from unusual central distances of amino acids

Prediction of functionally important residues in globular proteins from unusual central distances of amino acids

Well-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.

Using a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi.

Probabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.

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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%
发文量
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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