Petascale Integrative Approaches to Protein Structure Prediction

蛋白质结构预测的千万亿级综合方法

• 批准号: 1713695
• 负责人: Ken Dill
• 金额: $ 1.56万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713695&HistoricalAwards=false
• 关键词: Petascale; Integrative; Approaches; Protein; Structure

This project addresses a central challenge of biology: the prediction of protein structure from sequence. Accurate determination of protein structure is essential to understanding biological mechanism and designing new drugs. The purpose of this work is to use the petascale capabilities of Blue Waters to overcome limitations of current experimental and computational tools of structure determination. The project will do this by running a physics-based simulation method, called MELD (Modeling Employing Limited Data) that rationally incorporates sparse, noisy, and ambiguous experimental or heuristic information. Currently around 15% of protein targets cannot be tackled by known methods. The transformative goal of this work is to use MELD, running on Blue Waters, to break substantial barriers in size and complexity for computational structure prediction.This work has three major goals: (1) use MELD to provide structures for the known, small proteins were bioinformatics methods fail and where databases are sparse (e.g. membrane proteins), (2) provide kinetic and pathway information for protein folding by seeding Markov State Models (MSM) with MELD intermediate states, and (3) use accurate relative free energies of folding upon binding for more accurate peptide design. The project will use the the petascale capabilities of Blue Waters to push the size boundaries of full atomistic physics based predictions. Both the software and the results obtained from this project will be freely accessible to the larger scientific community.

这个项目解决了生物学的一个核心挑战：从序列预测蛋白质结构。准确测定蛋白质结构对了解生物机制和设计新药至关重要。这项工作的目的是利用Blue Waters的千兆级能力来克服当前结构确定的实验和计算工具的局限性。该项目将通过运行一种基于物理的模拟方法来实现这一目标，该方法被称为MELD（建模使用有限数据），该方法合理地结合了稀疏、嘈杂和模糊的实验或启发式信息。目前，大约15%的蛋白质目标无法用已知的方法处理。这项工作的变革目标是使用在Blue Waters上运行的MELD，打破计算结构预测在大小和复杂性方面的实质性障碍。这项工作有三个主要目标：(1)利用MELD为生物信息学方法失败且数据库稀疏的已知小蛋白质（例如膜蛋白）提供结构；(2)通过将MELD中间态嵌入马尔可夫状态模型（MSM）提供蛋白质折叠的动力学和途径信息；(3)利用结合时折叠的精确相对自由能进行更精确的肽设计。该项目将使用Blue Waters的千万亿级能力来推动基于全原子物理预测的尺寸界限。从这个项目中获得的软件和结果都将免费提供给更大的科学界。