III: Small: Quality Assessment of Computational Protein Models

III：小：计算蛋白质模型的质量评估

• 批准号: 0915801
• 负责人: Daisuke Kihara
• 金额: $ 32.76万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915801&HistoricalAwards=false
• 关键词: III; Small; Quality; Assessment; Computational

The PI plans to develop a new and robust hierarchical multi-resolution framework that can be used for assessement of model uncertainty, especially that associated with prediction of protein structure. The approaches also enable respresentation of the structure in ways that enable rapid searching of structure space. It is important to establish quality estimation methods for predicted structures so that they can be used wisely by knowing the limitations of the model. Protein tertiary structure prediction has made steady progress in the past decade. However, current prediction methods are still not capable of producing highly accurate models on a regular basis. Practical use of prediction methods by biologists is limited not only the accuracy of current prediction methods but also the lack of error estimation of the models they produce. Moderately accurate models are still useful for many purposes, including design of site-directed mutagenesis experiments and structure-based function prediction, if the possible error range is understood. Resulting quality assessment methods will also contribute to improvement of protein structure prediction methods. In addition, structure models of proteomes of model organisms will be constructed with quality assessment data and will be made available to the public through the Internet. The proposed project leverages Purdue University's efforts in interdisciplinary computational life science and engineering by training graduate students and undergraduate students of different backgrounds through interdisciplinary coursework and direct involvement in the project.

PI计划开发一种新的、强大的分层多分辨率框架，可用于评估模型的不确定性，特别是与蛋白质结构预测相关的不确定性。该方法还能够以能够快速搜索结构空间的方式重新呈现结构。 重要的是建立预测结构的质量估计方法，以便通过了解模型的局限性来明智地使用它们。蛋白质三级结构预测在过去的十年中取得了稳步的进展。然而，目前的预测方法仍然不能定期产生高度准确的模型。生物学家对预测方法的实际使用不仅受到当前预测方法的准确性的限制，而且还缺乏对它们产生的模型的误差估计。如果了解可能的误差范围，中等精度的模型仍然适用于许多目的，包括定点诱变实验的设计和基于结构的功能预测。 由此产生的质量评估方法也将有助于蛋白质结构预测方法的改进。 此外，将利用质量评估数据构建模式生物蛋白质组结构模型，并通过互联网向公众提供。拟议的项目利用普渡大学在跨学科计算生命科学和工程方面的努力，通过跨学科课程和直接参与该项目来培训不同背景的研究生和本科生。