Collaborative Research: ABI Development: Integrated platforms for protein structure and function predictions

合作研究：ABI开发：蛋白质结构和功能预测的集成平台

• 批准号: 1564756
• 负责人: Yang Zhang
• 金额: $ 83.32万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564756&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; Integrated

Proteins are the 'workhorse' molecules of life, they participate in nearly every activity that cells carry out. It follows that understanding protein structure and function is essential to understanding life processes, and how to control or modify them. Biochemistry and biophysics experiments give the most accurate data on protein structure and function, but the experiments are often expensive and too specialized for many of the cell and molecular biologists focused on a particular interesting protein. This means that reliable computational predictions of protein structure and function are in high demand. These techniques are also specialized but can be automated, which is the focus of this project, which aims to develop an integrated platform for high-resolution protein structure prediction and structure-based function annotation that is accessible from the Web. This resource will significantly enhance studies of individual proteins as well as processes in cellular biology and other biological sciences. Through the collaboration of the two institutions, students at NCAT will learn state of the art high performance computing methods, and workshops at both institutions will provide greater understanding of the capabilities of the new resource. Proteins are complex components of biological systems, and studies on their structure and function often require multiple approaches to measurement or modeling. Many of the advanced computer algorithms used in this modeling are highly specialized, involving a number of complicated processes for each aspect of the protein modeling. Biologists whose primary interest is the final result often cannot determine which algorithm or pipeline to choose, how to enter parameters, or how to interpret the resulting models. While continuing to improve the accuracy of the core algorithms in protein structure prediction and structure-based function annotation, this project will also make improvements to domain parsing and assembly, to improve the quality of complex protein structure and function modeling. Another major focus of this project is to develop new protocols that automatically guide protein targets to the most suitable pipelines. In conjunction with this there will be new confidence scoring systems, both global and local, to assist biological users as they interpret the modeling results. In addition, advanced parallel computing and graphic processor unit techniques will be implemented in order to accelerate the pipelines and reduce user's waiting time. New opportunities will be made for improving educational outcomes, in particular for women and minority students, in both University of Michigan and the North Carolina A&T State University. The on-line protein modeling system will be accessible to the community at http://zhanglab.ccmb.med.umich.edu.

蛋白质是生命的“主力”分子，它们几乎参与细胞进行的每种活动。因此，了解蛋白质结构和功能对于理解生命过程以及如何控制或修改它们至关重要。生物化学和生物物理学实验给出了有关蛋白质结构和功能的最准确数据，但是对于许多细胞和分子生物学家来说，实验通常很昂贵，并且太专注于特定有趣的蛋白质。这意味着蛋白质结构和功能的可靠计算预测需求量很高。这些技术也是专业化的，但可以自动化，这是该项目的重点，该项目旨在为高分辨率蛋白质结构预测和基于结构的功能注释开发一个集成平台，该平台可从网络访问。该资源将显着增强对单个蛋白质以及细胞生物学和其他生物科学过程的研究。通过这两个机构的合作，NCAT的学生将学习最先进的高性能计算方法，并且这两个机构的研讨会将提供对新资源能力的更多了解。蛋白质是生物系统的复杂组成部分，对其结构和功能的研究通常需要多种测量或建模的方法。该建模中使用的许多先进的计算机算法都是高度专业化的，涉及蛋白质建模的每个方面的许多复杂过程。主要兴趣是最终结果的生物学家通常无法确定要选择哪种算法或管道，如何输入参数或如何解释所得模型。在继续提高核心算法在蛋白质结构预测和基于结构的功能注释中的准确性，该项目还将改善域解析和组装，以提高复杂蛋白质结构和功能建模的质量。该项目的另一个主要重点是开发新方案，以自动指导蛋白质目标到最合适的管道。结合使用，将有新的信心评分系统，包括全球和本地的系统，以帮助生物使用者解释建模结果。此外，将实现高级并行计算和图形处理器单元技术，以加速管道并减少用户的等待时间。 在密歇根大学和北卡罗来纳州A＆T州立大学，将有新的机会来改善教育成果，特别是妇女和少数民族学生。在线蛋白质建模系统将在http://zhanglab.ccmb.med.umich.edu上访问。