25年的蛋白质块之旅:揭示结构字母表的多功能性。

IF 3
Bernard Offmann, Alexandre G de Brevern
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引用次数: 0

摘要

蛋白质块(PBs)代表了一种广泛使用的结构字母表,可以通过由二面角定义的16个原型片段近似和分析局部蛋白质构象。PBs最初是为了克服经典二级结构定义的局限性而开发的,它为理解蛋白质的结构、动力学和功能提供了强大的工具。其应用领域包括结构标注、蛋白质折叠叠加与识别、序列预测和分子动力学分析等。值得注意的是,PBs通过基于熵的指数(Neq)促进了刚性,柔性和无序区域的区分,提供了对蛋白质灵活性和内在无序的见解。它们与深度学习的结合极大地提高了预测性能,并在整合素多态性、VHH可变性和AlphaFold结构分析等多种环境中得到了证明。作为一个强大的和适应性强的框架,PBs仍然是现代结构生物信息学的核心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 25-year journey with protein blocks: Unveiling the versatility of a structural alphabet.

Protein Blocks (PBs) represent a widely used structural alphabet that enables the approximation and analysis of local protein conformations through 16 prototype fragments defined by dihedral angles. Initially developed to overcome the limitations of classical secondary structure definitions, PBs provide a powerful tool for understanding protein structure, dynamics, and function. Their applications span structural annotation, protein fold superimposition and recognition, sequence-based prediction and molecular dynamics analysis. Notably, PBs facilitate the distinction between rigid, flexible, and disordered regions via an entropy-based index (Neq), offering insights into protein flexibility and intrinsic disorder. Their integration with deep learning has dramatically improved predictive performance, and their utility has been demonstrated in diverse contexts such as integrin polymorphisms, VHH variability and AlphaFold structure analysis. As a robust and adaptable framework, PBs remain central in modern structural bioinformatics.

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