Postdoctoral Research Fellowships in Biology for FY 2009

2009财年生物学博士后研究奖学金

• 批准号: 0906026
• 负责人: Firas Khatib
• 金额: $ 12.3万
• 依托单位: Khatib Firas D
• 依托单位国家: 美国
• 项目类别: Fellowship
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-11-01 至 2011-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0906026&HistoricalAwards=false
• 关键词: Postdoctoral; Research; Fellowships; Biology; FY

This action funds an NSF Postdoctoral Research Fellowship for FY 2009. The fellowship supports a research and training plan entitled "Automating human intuition to improve protein structure prediction" for Firas Khatib. The host institution for this research is the University of Washington, and the sponsoring scientist is Dr. David Baker.This project uses the combined power of humans and computers to build accurate models of disease-related proteins. Determining the correct structure of a protein helps researchers successfully target them with drugs. Experimental methods for solving proteins are time consuming and expensive, making a computational solution to the protein-folding problem necessary. Even with the current supercomputing and distributed computing power available, new algorithms are needed to solve one of the main computational bottlenecks, conformational sampling. Because a protein chain can have so many different possible conformations, efficient exploration of the search space is crucial. A new approach to this problem is the use of human puzzle-solving intuition and pattern recognition skills combined with computers running Rosetta, the prediction algorithm developed in David Baker's research group. Foldit is an interactive Rosetta-based program allowing users around the world to directly manipulate protein structure models on their home computers in real-time. The ultimate goal of Foldit is to use the results from the computer program to improve structure prediction algorithms by capitalizing on natural human 3D problem-solving skills. The project determines what aspect of the structure prediction problem game-playing humans can improve and implement these new techniques into Rosetta's automated protocol. The successful automation of human intuition would be a remarkable achievement in computational science. Training objectives include development of programming and computational skills as well as teaching and mentoring abilities. Foldit users are able to share their solutions with others around the world, taking advantage of the strengths that different people have, in order to improve results globally. Presentations on Foldit are being given to children and parents at the local science and educational museum and at local high schools in the hope that Foldit could be used in chemistry courses around the world, creating the next generation of protein folders, and then in turn new structure prediction algorithms could be developed based on these new experts' protein-folding skills. This gives worldwide participants an immediate and relevant application for protein-folding knowledge, hopefully reinforcing the motivation to understand the underlying science. In addition, because anyone in the world can contribute to Foldit, this research establishes a global collaborative network. The algorithms and methods developed will be published, and the Rosetta software source code will be freely and openly available to the scientific community.

这项行动资助了 2009 财年 NSF 博士后研究奖学金。该奖学金支持 Firas Khatib 题为“自动化人类直觉以改善蛋白质结构预测”的研究和培训计划。这项研究的主办机构是华盛顿大学，资助科学家是 David Baker 博士。该项目利用人类和计算机的综合力量来建立疾病相关蛋白质的精确模型。确定蛋白质的正确结构有助于研究人员成功地用药物靶向它们。解决蛋白质问题的实验方法既耗时又昂贵，因此需要通过计算解决蛋白质折叠问题。 即使拥有当前的超级计算和分布式计算能力，仍然需要新的算法来解决主要计算瓶颈之一，即构象采样。由于蛋白质链可以具有多种不同的可能构象，因此有效探索搜索空间至关重要。解决这个问题的一种新方法是利用人类解谜直觉和模式识别技能与运行 Rosetta 的计算机相结合，Rosetta 是 David Baker 研究小组开发的预测算法。 Foldit 是一个基于 Rosetta 的交互式程序，允许世界各地的用户直接在家用计算机上实时操作蛋白质结构模型。 Foldit 的最终目标是利用计算机程序的结果，通过利用人类自然的 3D 问题解决技能来改进结构预测算法。该项目确定玩游戏的人类可以改进结构预测问题的哪些方面并将这些新技术应用到 Rosetta 的自动化协议中。人类直觉的成功自动化将是计算科学的一项了不起的成就。培训目标包括编程和计算技能以及教学和指导能力的发展。 Foldit 用户能够与世界各地的其他人分享他们的解决方案，利用不同人的优势，以改善全球的结果。 当地科教博物馆和当地高中正在向儿童和家长进行 Foldit 演示，希望 Foldit 能够用于世界各地的化学课程，创造下一代蛋白质文件夹，进而推出新的蛋白质文件夹。可以根据这些新专家的蛋白质折叠技能开发结构预测算法。这为全世界的参与者提供了蛋白质折叠知识的直接且相关的应用，有望增强理解基础科学的动力。此外，由于世界上任何人都可以为 Foldit 做出贡献，因此这项研究建立了一个全球协作网络。开发的算法和方法将被公布，Rosetta软件源代码将免费、公开地提供给科学界。