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探讨基于 TSR 的蛋白质 3-D 结构比对方法在蛋白质聚类、结构基序识别和发现蛋白激酶、水解酶以及 SARS-CoV-2 蛋白方面的有效性,方法是通过氨基酸分组的应用。

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping.

机构信息

The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.

High Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, LA 70803, USA.

出版信息

Comput Biol Chem. 2021 Jun;92:107479. doi: 10.1016/j.compbiolchem.2021.107479. Epub 2021 Mar 29.

Abstract

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.

摘要

开发蛋白质三维结构比较方法对于理解蛋白质功能至关重要。有些氨基酸具有结构相似性,而有些氨基酸则有很大的差异。这些结构决定了氨基酸的化学和物理性质。将具有相似结构的氨基酸分组可以提高识别结构保守区域的能力,并增加蛋白质之间的全局结构相似性。我们系统地研究了氨基酸分组对特定/特定、常见/常见和统计上不同键数量的影响,以更好地理解蛋白质结构关系。常见键代表在所有类型的蛋白质中都存在的子结构,而特定键代表在数据集的某一类蛋白质中特有的子结构。我们的研究结果表明,在基于三角空间关系(TSR)的方法中应用氨基酸分组来计算蛋白质之间的结构相似性,可以在某些情况下提高蛋白质聚类的准确性。此外,应用氨基酸分组有助于识别或发现保守结构基序。主成分分析(PCA)的结果表明,应用氨基酸分组比不使用氨基酸分组时稍微能捕捉到更多的结构变化,这表明氨基酸分组如预期的那样减少了结构多样性。基于 TSR 的方法独特地识别和发现药物或相互作用蛋白质的结合位点。我们定义的 SARS-CoV-2、SARS-CoV 和 MERS-CoV 的 nsp16 的结合位点将有助于未来设计抗病毒药物,以提高治疗效果。将氨基酸分组功能纳入我们的结构比较方法的这种方法具有很大的潜力,并提供了对蛋白质结构关系的深入理解。

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