Petascale Integrative Approaches to Protein Structure Prediction

该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。蛋白质结构的准确测定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千万亿次的能力，蓝色沃茨，以克服目前的实验和计算工具的结构测定的局限性。 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，该方法称为MELD（采用有限数据建模），它合理地结合了稀疏，嘈杂和模糊的实验或启发式信息。目前，大约15%的蛋白质靶标无法通过已知方法处理。这项工作的变革性目标是使用在Blue沃茨上运行的MELD，打破计算结构预测在规模和复杂性方面的实质性障碍。这项工作有三个主要目标：（1）在生物信息学方法失败且数据库稀疏的情况下，使用MELD为已知的小蛋白质提供结构（例如膜蛋白），（2）通过用MELD中间状态接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，和（3）在结合时使用精确的相对折叠自由能以进行更精确的肽设计。该项目将使用Blue沃茨的千万亿次能力来推动基于全原子物理学的预测的大小边界。 该项目的软件和成果将免费提供给广大科学界。

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.