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.

该项目解决了生物学的一个核心挑战：根据序列预测蛋白质结构。准确测定蛋白质结构对于理解生物学机制和设计新药至关重要。这项工作的目的是利用 Blue Waters 的千万亿级能力来克服当前结构测定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点，称为 MELD（使用有限数据建模），该方法合理地结合了稀疏、噪声和模糊的实验或启发式信息。目前，已知方法无法解决约 15% 的蛋白质靶标。这项工作的变革性目标是使用在 Blue Waters 上运行的 MELD 来打破计算结构预测的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）使用 MELD 为已知的小蛋白质提供结构如果生物信息学方法失败并且数据库稀疏（例如膜蛋白），（2）通过使用 MELD 中间态播种马尔可夫状态模型（MSM）来提供蛋白质折叠的动力学和途径信息，以及（3）使用准确的相对自由能结合时折叠，以实现更准确的肽设计。该项目将利用 Blue Waters 的千万亿级能力来突破基于原子物理的预测的尺寸界限。 该软件和从该项目获得的结果都将免费供更大的科学界使用。

项目成果

NMR‐assisted protein structure prediction with MELDxMD

使用 MELDxMD 进行 NMR 辅助蛋白质结构预测

• DOI: 10.1002/prot.25788
• 发表时间: 2019-08
• 期刊: and Bioinformatics
• 作者: Robertson, James C.;Nassar, Roy;Liu, Cong;Brini, Emiliano;Dill, Ken A.;Perez, Alberto
• 通讯作者: Perez, Alberto

MELD-Path Efficiently Computes Conformational Transitions, Including Multiple and Diverse Paths

MELD-Path 有效计算构象跃迁，包括多个不同的路径

• DOI: 10.1021/acs.jctc.7b01294
• 发表时间: 2018-04-10
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Perez A;Sittel F;Stock G;Dill K
• 通讯作者: Dill K

Predicting Protein Dimer Structures Using MELD × MD

使用 MELD — MD 预测蛋白质二聚体结构

• DOI: 10.1021/acs.jctc.8b01208
• 发表时间: 2019-04
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Brini, Emiliano;Kozakov, Dima;Dill, Ken A.
• 通讯作者: Dill, Ken A.

MELD × MD Folds Nonthreadables, Giving Native Structures and Populations

MELD — MD 折叠不可线程，提供原生结构和群体

• DOI: 10.1021/acs.jctc.8b00886
• 发表时间: 2018-11
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Robertson, James C.;Perez, Alberto;Dill, Ken A.
• 通讯作者: Dill, Ken A.