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

蛋白质是生命的“主力”分子，它们几乎参与了细胞进行的每一项活动。因此，了解蛋白质的结构和功能对于了解生命过程以及如何控制或修改它们是必不可少的。生物化学和生物物理实验提供了关于蛋白质结构和功能的最准确的数据，但对于许多专注于特定感兴趣蛋白质的细胞和分子生物学家来说，这些实验往往昂贵且过于专业化。这意味着对蛋白质结构和功能的可靠计算预测的需求很高。这些技术也是专门化的，但可以自动化，这是本项目的重点，旨在开发一个可从Web访问的高分辨率蛋白质结构预测和基于结构的功能注释的集成平台。这一资源将大大加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，两个机构的研讨会将提供对新资源功能的更多了解。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往往需要多种方法来测量或建模。这种建模中使用的许多高级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。生物学家的主要兴趣是最终结果，他们往往无法确定选择哪种算法或管道，如何输入参数，或如何解释结果模型。在继续提高蛋白质结构预测和基于结构的功能标注核心算法准确性的同时，本项目还将在领域解析和组装方面进行改进，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关注点是开发新的协议，自动将蛋白质靶标引导到最合适的管道。与此相结合，将有新的全球和局部置信度评分系统，以帮助生物用户解释建模结果。此外，还将采用先进的并行计算和图形处理器单元技术，以加快流水线速度，减少用户的等待时间。密歇根大学和北卡罗来纳农工州立大学将创造新的机会来改善教育结果，特别是对女性和少数族裔学生。在线蛋白质建模系统将在http://zhanglab.ccmb.med.umich.edu.上向社区开放

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.