A three dimensional visualisation approach to protein heavy-atom structure reconstruction

Q3 Biochemistry, Genetics and Molecular Biology
Xubiao Peng, Alireza Chenani, Shuangwei Hu, Yifan Zhou, Antti J Niemi
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引用次数: 21

Abstract

A commonly recurring problem in structural protein studies, is the determination of all heavy atom positions from the knowledge of the central α-carbon coordinates.

We employ advances in virtual reality to address the problem. The outcome is a 3D visualisation based technique where all the heavy backbone and side chain atoms are treated on equal footing, in terms of the Cα coordinates. Each heavy atom is visualised on the surfaces of a different two-sphere, that is centered at another heavy backbone and side chain atoms. In particular, the rotamers are visible as clusters, that display a clear and strong dependence on the underlying backbone secondary structure.

We demonstrate that there is a clear interdependence between rotameric states and secondary structure. Our method easily detects those atoms in a crystallographic protein structure which are either outliers or have been likely misplaced, possibly due to radiation damage. Our approach forms a basis for the development of a new generation, visualization based side chain construction, validation and refinement tools. The heavy atom positions are identified in a manner which accounts for the secondary structure environment, leading to improved accuracy.

Abstract Image

蛋白质重原子结构重建的三维可视化方法
结构蛋白研究中经常出现的一个问题是,根据中心α-碳坐标确定所有重原子的位置。我们利用先进的虚拟现实技术来解决这个问题。结果是一种基于3D可视化的技术,其中所有重主链和侧链原子在Cα坐标下被平等对待。每个重原子在不同的双球表面上被可视化,该双球以另一个重主链和侧链原子为中心。特别是,旋转体以团簇的形式可见,显示出对底层主干二级结构的清晰而强烈的依赖。我们证明了旋转美态和二级结构之间存在明显的相互依存关系。我们的方法很容易检测到晶体学蛋白质结构中的那些原子,这些原子要么是异常值,要么可能是由于辐射损伤而错位的。我们的方法为新一代基于可视化的侧链构建、验证和改进工具的开发奠定了基础。重原子位置的识别方式考虑了二级结构环境,从而提高了精度。
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来源期刊
CiteScore
3.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
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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