"High-throughput characterization, prediction, and applications of protein disorder"

蛋白质是由一个或多个氨基酸链组成的生物大分子，几乎参与细胞内的每一个过程。多年来，科学家们一直坚信，这些链必须折叠成精确、坚硬的分子，才能使蛋白质正常运行。这种观点现在正在改变。这些本质上无序的蛋白质至少有一些无序(也称为未折叠/高度柔性)部分，其中许多蛋白质在执行其功能时从未完全折叠成刚性分子。这种灵活性可能帮助了地球上的生命开始，并对细胞信号转导、细胞分裂调节、转录、翻译、磷酸化和许多其他细胞过程至关重要。这种疾病在自然界中非常丰富，其流行率在几种人类疾病中都有表现。这意味着无序在决定蛋白质功能方面的作用不容忽视。然而，对蛋白质紊乱的注释和表征落后于快速增长的已知蛋白质数量。对无序的实验注释既耗时又困难，因此从蛋白质序列预测无序的计算方法已经成为弥合注释差距和研究无序的可行选择。尽管这些预测指标的质量在继续提高，但迫切需要更准确的方法和解决障碍具体特征的新方法。此外，迫切需要了解和描述各种蛋白质组和蛋白质功能类别的紊乱。为此，我们的目标包括(1)开发一个全面的计算平台，用于准确、快速和多目标地预测无序；以及(2)无序预测的应用和实验验证。这项工作有助于更全面地了解蛋白质的无序、蛋白质折叠的原理以及蛋白质功能的分子机制。我们的方法为指导实验者提供了一种成本和时间有效的解决方案，它们对于几个领域的现代研究和开发至关重要，包括合理的药物设计、结构基因组学和系统生物学。

Proteins are bio-macromolecules composed of one or more amino acids chains which participate in virtually every process within cells. For years, scientists were convinced that these chains must fold into precise, rigid molecules to allow proteins to function correctly. This view is changing now. The intrinsically disordered proteins have at least some disordered (also called unfolded/highly flexible) parts and many of them carry out their function without ever fully folding into a rigid molecule. This flexibility possibly helped life on earth get started and is crucial for cellular signal transduction, regulation of cell division, transcription, translation, phosphorylation, and many other cellular processes. The disorder is highly abundant in nature and its prevalence was shown in several human diseases. This means that the roles of disorder in determining protein function cannot be ignored. However, the annotation and characterization of protein disorder is lagging behind the rapidly growing number of known proteins. Experimental annotations of disorder are time consuming and difficult and thus computational methods that predict disorder from protein sequences have emerged as a viable alternative to bridge the annotation gap and to investigate the disorder. Although the quality of these predictors continues to rise, more accurate methods and novel methods that address specific characteristics of disorder are urgently needed. Moreover, there is a pressing need to understand and characterize disorder in various proteomes and functional classes of proteins. To this end, our objectives include (1) development of a comprehensive computational platform for accurate, fast, and multi-objective prediction of disorder; and (2) applications and experimental validation of disorder predictions. This work facilitates a more complete understanding of the protein disorder, principles of protein folding, and molecular mechanisms of protein function. Our methods provide a cost and time effective solution to guide experimentalists, and they are crucial for modern research and development in several areas, including rational drug design, structural genomics, and systems biology.