Discovering protein structure and function through nonlinear system identification

通过非线性系统识别发现蛋白质结构和功能

• 批准号: 327498-2006
• 负责人: Green, James
• 金额: $ 1.09万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=397806
• 关键词: Discovering; protein; structure; function; through

Predicting protein structure and function from sequence is of fundamental importance to biomedical research. An effective solution has the potential to accelerate the drug discovery process and may lead to an increased understanding of cancer and other disease processes. Unfortunately, experimental methods for elucidating a protein's structure are very costly and are not always applicable. Computational prediction techniques provide an attractive alternative; however, the accurate prediction of 3D tertiary protein structure directly from amino acid sequence data continues to elude researchers when highly similar previously solved protein structures are not available or for longer domains. Even then, knowing a protein's tertiary structure may not be sufficient to fully understand its function: the majority of eukaryotic proteins are not functional in their original structure and must be activated through some form of post-translational modification (PTM). We have recently developed a novel approach to protein secondary structure prediction making use of Parallel Cascade Identification (PCI), a powerful method of nonlinear system identification. Resulting accuracy not only compared well with top contemporary prediction methods based on neural networks and hidden Markov models, but PCI was also shown to be highly effective when used in concert with such methods. While predicting secondary structure is a useful intermediate step, the ultimate goal of protein structure prediction is to predict the complete 3D structure of the active conformation(s) of a protein, including PTM information, given knowledge only of its amino acid sequence and its environment. Building on the established success of PCI as a proteomics tool, the research program proposed herein will make significant progress in these new and challenging areas, and make a real contribution towards in silico drug prediction of protein structure and function.

从序列预测蛋白质的结构和功能对于生物医学研究具有重要意义。有效的解决方案有可能加速药物发现过程，并可能导致对癌症和其他疾病过程的更多了解。不幸的是，用于阐明蛋白质结构的实验方法非常昂贵，并且并不总是适用。计算预测技术提供了一个有吸引力的替代方案;然而，直接从氨基酸序列数据中准确预测3D三级蛋白质结构仍然困扰着研究人员，当高度相似的先前解决的蛋白质结构不可用或更长的结构域时。即使这样，知道蛋白质的三级结构可能不足以完全理解其功能：大多数真核生物蛋白质在其原始结构中没有功能，必须通过某种形式的翻译后修饰（PTM）激活。我们最近开发了一种新的方法来预测蛋白质二级结构利用并行级联识别（PCI），一个强大的非线性系统识别方法。结果的准确性不仅与基于神经网络和隐马尔可夫模型的当代顶级预测方法相比，而且PCI在与这些方法配合使用时也被证明是非常有效的。虽然预测二级结构是一个有用的中间步骤，但蛋白质结构预测的最终目标是预测蛋白质活性构象的完整3D结构，包括PTM信息，仅考虑其氨基酸序列及其环境的知识。基于PCI作为蛋白质组学工具的既定成功，本文提出的研究计划将在这些新的和具有挑战性的领域取得重大进展，并对蛋白质结构和功能的计算机药物预测做出真实的贡献。