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

蛋白质是生命的“主力”分子，它们几乎参与细胞进行的所有活动。因此，了解蛋白质的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提供了关于蛋白质结构和功能的最准确的数据，但这些实验通常是昂贵的，而且对于许多专注于特定蛋白质的细胞和分子生物学家来说，这些实验过于专业化。这意味着对蛋白质结构和功能的可靠计算预测的需求很大。这些技术也是专门的，但可以自动化，这是这个项目的重点，它旨在开发一个集成的平台，用于高分辨率的蛋白质结构预测和基于结构的功能注释，可从Web访问。这一资源将显著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。通过两所机构的合作，NCAT的学生将学习最先进的高性能计算方法，两所机构的研讨会将更好地了解新资源的功能。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。在这种建模中使用的许多先进的计算机算法都是高度专业化的，涉及到蛋白质建模的每个方面的许多复杂过程。对最终结果感兴趣的生物学家通常无法决定选择哪种算法或管道，如何输入参数，或者如何解释结果模型。本项目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精度的同时，还将对领域解析和组装进行改进，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重点是开发新的协议，自动引导蛋白质目标到最合适的管道。与此同时，将会有新的全球和本地的信心评分系统，以帮助生物学用户解释建模结果。此外，先进的并行计算和图形处理器单元技术将被实施，以加快管道和减少用户的等待时间。在密歇根大学和北卡罗来纳农工州立大学，将为改善教育成果创造新的机会，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对社区开放。

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.