An improved protein structure evaluation using a semi-empirically derived structure property

IF 2.222 Q3 Biochemistry, Genetics and Molecular Biology BMC Structural Biology Pub Date : 2018-12-12 DOI:10.1186/s12900-018-0097-0
Manoj Kumar Pal, Tapobrata Lahiri, Garima Tanwar, Rajnish Kumar
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引用次数: 2

Abstract

In the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.

It was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.

In this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.

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利用半经验推导的结构性质改进蛋白质结构评价
在已知的实验和理论技术限制下难以获得蛋白质结构的背景下,仍然需要一种快速准确的蛋白质结构评估方法,以大幅减少已知序列和结构之间的巨大差距。在目前实践的理论技术中,以分子动力学优化为基础的同源性建模似乎是最受欢迎的一种。然而,它受到来自一组针对特定靶蛋白预测的蛋白质模型产生的不同验证参数的矛盾指示的影响。例如,在一个模型中,Ramachandran分数可能很高,使其可以接受,然而,它的势能可能不是很低,使其不可接受,反之亦然。为了解决这个问题,本研究的主要目的是利用一个简单的实验导出的输出,未知结构的有关蛋白质的表面粗糙度指数作为干预剂,可以通过使用相同蛋白质的热变性聚集体的普通显微镜图像获得。有趣的是,观察到有关蛋白质的直接实验知识,无论多么简单,都可能从数据库驱动的结构预测比较技术产生的一组混乱的模型中,深入了解其特定结构模型的可接受性。结果表明,通过简单的实验输出,可以进一步提高蛋白质结构评估的速度,而不影响准确性。本研究提供了一种改进蛋白质结构评价的半经验方法。结果表明,通过同源性技术获得蛋白质的结构模型后,可以利用以同一蛋白质为实验原料,通过实验直接获得的结构剂,从一组帕累托最优结构中选择最佳模型的问题得到解决。总的来说,在获得引起流行病或生物战的病原体的合理准确的蛋白质结构的背景下,这种方法可能作为快速药物设计的可行解决方案。
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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.
期刊最新文献
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