Three-Dimensional Molecular Modeling of a Diverse Range of SC Clan Serine Proteases.

Molecular biology international Pub Date : 2012-01-01 Epub Date: 2012-11-19 DOI:10.1155/2012/580965
Aparna Laskar, Aniruddha Chatterjee, Somnath Chatterjee, Euan J Rodger
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引用次数: 6

Abstract

Serine proteases are involved in a variety of biological processes and are classified into clans sharing structural homology. Although various three-dimensional structures of SC clan proteases have been experimentally determined, they are mostly bacterial and animal proteases, with some from archaea, plants, and fungi, and as yet no structures have been determined for protozoa. To bridge this gap, we have used molecular modeling techniques to investigate the structural properties of different SC clan serine proteases from a diverse range of taxa. Either SWISS-MODEL was used for homology-based structure prediction or the LOOPP server was used for threading-based structure prediction. The predicted models were refined using Insight II and SCRWL and validated against experimental structures. Investigation of secondary structures and electrostatic surface potential was performed using MOLMOL. The structural geometry of the catalytic core shows clear deviations between taxa, but the relative positions of the catalytic triad residues were conserved. Evolutionary divergence was also exhibited by large variation in secondary structure features outside the core, differences in overall amino acid distribution, and unique surface electrostatic potential patterns between species. Encompassing a wide range of taxa, our structural analysis provides an evolutionary perspective on SC clan serine proteases.

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多种SC族丝氨酸蛋白酶的三维分子建模。
丝氨酸蛋白酶参与多种生物过程,并被归类为具有结构同源性的氏族。虽然SC族蛋白酶的各种三维结构已被实验确定,但它们主要是细菌和动物蛋白酶,有一些来自古生菌、植物和真菌,尚未确定原生动物的结构。为了弥补这一差距,我们使用分子建模技术来研究来自不同分类群的不同SC族丝氨酸蛋白酶的结构特性。使用SWISS-MODEL进行基于同构的结构预测,或使用LOOPP服务器进行基于线程的结构预测。使用Insight II和SCRWL对预测模型进行了改进,并针对实验结构进行了验证。用MOLMOL对其二级结构和静电表面电位进行了研究。催化核的几何结构在分类群之间有明显的差异,但催化三残基的相对位置是保守的。进化差异还表现在核心外二级结构特征的巨大差异、氨基酸总体分布的差异以及物种间独特的表面静电势模式。包含广泛的分类群,我们的结构分析提供了SC族丝氨酸蛋白酶的进化视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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