BACKGROUND 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. RESULT 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. CONCLUSION 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.

中文翻译：

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使用半经验导出的结构特性改进的蛋白质结构评估。

背景技术在挑战性的实验和理论技术限制下获得蛋白质结构的背景下，仍然需要快速且准确的蛋白质结构评估方法来大幅减少已知序列和结构之间的巨大差距。在当前实践的理论技术中，以基于分子动力学的优化为后盾的同源性建模似乎是最受欢迎的一种。然而，其遭受从针对特定靶蛋白预测的一组蛋白质模型产生的不同验证参数的矛盾指示。例如，在一个模型中，Ramachandran得分可能很高，因此可以接受，而其势能可能不是很低，因此它不能被接受，反之亦然。为了解决这个问题，这项研究的主要目的是固定的，以便利用简单的实验得出的结果，将未知结构的相关蛋白质的表面粗糙度指数用作干预剂，该干预剂可以使用相同蛋白质的热变性聚集体的普通显微图像获得。结果令人感兴趣的是，观察到有关蛋白质的直接实验知识（无论多么简单）可能会从数据库驱动的用于结构预测的比较技术生成的混乱模型集中，了解其特定结构模型的可接受性。从种类繁多的蛋白质结构类别中获得的结果表明，通过招募简单的实验输出，可以进一步提高蛋白质结构评估的速度，而不会影响准确性。结论在这项工作中，提供了一种半经验方法论方法来改善蛋白质结构评估。结果表明，一旦通过同源技术获得了蛋白质的结构模型，就可以通过使用通过对相同蛋白质进行实验而直接获得的结构试剂来解决从帕累托最优结构的混乱集中选择最佳模型的问题。作为实验成分。总体而言，在获得引起流行病或生物战的病原体的蛋白质结构合理准确的背景下，这种方法可以用作快速药物设计的合理解决方案。从帕累托最优结构的混乱集合中选择最佳模型的问题可以通过使用可直接通过实验获得的结构试剂来解决，该结构试剂可以使用与实验成分相同的蛋白质进行实验。总体而言，在获得引起流行病或生物战的病原体合理合理的蛋白质结构的背景下，这种方法可用作快速药物设计的合理解决方案。从帕累托最优结构的混乱集合中选择最佳模型的问题可以通过使用可直接通过实验获得的结构试剂来解决，该结构试剂可以使用与实验成分相同的蛋白质进行实验。总体而言，在获得引起流行病或生物战的病原体的蛋白质结构合理准确的背景下，这种方法可以用作快速药物设计的合理解决方案。