Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.
PLoS Comput Biol. 2023 Oct 16;19(10):e1011565. doi: 10.1371/journal.pcbi.1011565. eCollection 2023 Oct.
Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder-high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length-are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.
理解蛋白质序列如何赋予功能仍然是分子生物学中的一个决定性挑战。两种方法已经产生了巨大的洞察力，但通常是分开进行的：基于结构的方法，其中序列编码的结构介导功能；以及基于无序的方法，其中序列决定理化和动力学特性，从而在没有稳定结构的情况下决定功能。在这里，我们研究了带高电荷的蛋白质区域（>40%带电荷的残基），这些区域通常被认为是无序的。利用结构预测和实验结构的最新进展，我们表明，这些区域中大约有 40%形成了结构良好的螺旋。通常用于预测无序的特征——高电荷密度、低疏水性、低序列复杂性和进化上变化的长度——也与溶剂化的、可变长度的螺旋兼容。我们表明，一个简单的组成分类器预测结构的存在的能力远远优于基于电荷和疏水性的成熟启发式方法。我们表明，在不同蛋白质组的高电荷区域中，螺旋结构比以前认为的更为普遍，并描述了高电荷区域的保守性。我们的结果强调了在结构和无序方法之间进行整合而不是选择的重要性。
