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

“蛋白质紊乱的高通量表征、预测和应用”

• 批准号: 298328-2012
• 负责人: Kurgan, Lukasz
• 金额: $ 2.48万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=548301
• 关键词: throughput; characterization; prediction; applications; protein

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.

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，科学家们相信这些链必须折叠成精确的刚性分子，才能让蛋白质正确发挥功能。这种观点现在正在改变。内在无序的蛋白质至少有一些无序（也称为未折叠/高度柔性）的部分，其中许多蛋白质在没有完全折叠成刚性分子的情况下发挥其功能。这种灵活性可能帮助地球上的生命开始，并且对于细胞信号转导，细胞分裂，转录，翻译，磷酸化和许多其他细胞过程的调节至关重要。这种疾病在自然界中非常丰富，并且在几种人类疾病中显示出其患病率。这意味着，在决定蛋白质功能的障碍的作用不容忽视。然而，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。实验注释的障碍是耗时和困难的，因此计算方法，预测从蛋白质序列的障碍已经成为一个可行的替代桥梁的注释差距，并调查的障碍。虽然这些预测因子的质量不断提高，但迫切需要更准确的方法和新方法来解决疾病的特定特征。此外，迫切需要了解和表征各种蛋白质组和蛋白质功能类别中的紊乱。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，快速和多目标的无序预测;（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紊乱，蛋白质折叠的原理和蛋白质功能的分子机制。我们的方法提供了一个成本和时间有效的解决方案，以指导实验，他们是至关重要的现代研究和开发在几个领域，包括合理的药物设计，结构基因组学和系统生物学。