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CAREER: Reinforcement Learning of the Free Energy Landscapes of Proteins

职业：蛋白质自由能景观的强化学习

基本信息

批准号：
1845606
负责人：
Diwakar Shukla
金额：
$ 69.97万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-12-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845606&HistoricalAwards=false
关键词：
CAREER Reinforcement Learning Free Energy

项目摘要

Proteins are molecular machines that perform a variety of tasks in living organisms, such as transporting nutrients and communicating signals within and between cells. Some of these proteins undergo a change in their conformation or shape to carry out these functions. It is challenging to visualize the different shapes of these molecular machines using traditional experimental techniques, such as X-ray crystallography. Therefore, the molecular level understanding of these processes and its implications for protein function remains elusive. This lack of molecular information makes it difficult to develop engineering approaches to regulate protein function. Molecular Dynamics simulations provide a way to observe these movements at the atomic scale. However, significant amount of computer time is required to observe long timescale conformational change processes in proteins. The objectives of this project are (1) to develop efficient algorithms based on machine learning techniques to understand how proteins undergo conformational change and (2) to apply these algorithms to understand the role of protein dynamics in transport of molecules across the cell membrane via membrane transporters. The specific transporters investigated in this project play a critical role in determining crop productivity and neurological disorders in humans. In concert with these research objectives, the PI will develop outreach activities teaching high school girls about computational methods used to investigate protein function via Girl's Adventure in Mathematics, Engineering and Sciences summer camp at the University of Illinois. PI also plans to engage African-American boys at local Urbana-Champaign schools via a three-day after-school program to teach them about protein structure and function.This project will develop computational methods that can efficiently explore the free energy landscapes associated with protein conformational changes. This work is guided by the hypothesis that leveraging ideas from reinforcement learning technique and using evolutionary coupled residue pair distances as order parameters for protein functional dynamics will allow efficient sampling of free energy landscapes. The development of the algorithm called "Reinforcement Learning-Based Adaptive Sampling" (REAP) has been initiated. Preliminary results have shown promising application of these ideas to several proteins. The fully developed algorithm would be particularly useful for systems with limited structural information, as order parameters can be identified using evolutionary coupled residues based on sequence information alone. The specific goals of this project include: (1) further develop the REAP methodology to efficiently explore the free energy landscapes associated with protein function, (2) test the new methodology to understand molecular processes of high biological importance but with limited availability of structural information. In particular, molecular mechanisms of substrate transport and its regulation for the following transport processes will be investigated: (1) Nitrate transport process via root-associated transporters in plants with applications in increasing the crop yields. (2) Serotonin transport in brain via human Serotonin transporter for elucidating molecular origin of neurological disorders. (3) Sugar transport via SWEET family transporter in Rice with applications in enhancing plant growth. Despite differences in their function and structures, these systems share similarities in terms of their modes of regulation that could allow for a comprehensive understanding of the regulatory mechanisms in membrane transporters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质是在生物体中执行各种任务的分子机器，例如在细胞内和细胞间运输营养物质和传递信号。这些蛋白质中的一些经历了它们的构象或形状的改变来执行这些功能。使用传统的实验技术，如x射线晶体学，可视化这些分子机器的不同形状是具有挑战性的。因此，对这些过程的分子水平理解及其对蛋白质功能的影响仍然难以捉摸。分子信息的缺乏使得开发调节蛋白质功能的工程方法变得困难。分子动力学模拟提供了一种在原子尺度上观察这些运动的方法。然而，要观察蛋白质长时间的构象变化过程，需要大量的计算机时间。该项目的目标是：(1)开发基于机器学习技术的高效算法，以了解蛋白质如何经历构象变化；(2)应用这些算法来了解蛋白质动力学在分子通过膜转运体跨细胞膜运输中的作用。该项目中研究的特定转运蛋白在决定作物生产力和人类神经系统疾病中发挥关键作用。为了配合这些研究目标，PI将在伊利诺伊大学开展拓展活动，通过“女孩在数学、工程和科学中的冒险”夏令营，向高中女生传授用于研究蛋白质功能的计算方法。PI还计划通过一个为期三天的课后项目，让当地厄巴纳-香槟学校的非裔美国男孩参与进来，教授他们蛋白质的结构和功能。该项目将开发计算方法，可以有效地探索与蛋白质构象变化相关的自由能景观。这项工作的指导假设是，利用强化学习技术的思想，并使用进化耦合残基对距离作为蛋白质功能动力学的阶参数，将允许对自由能景观进行有效采样。一种名为“基于强化学习的自适应采样”（REAP）的算法已经开始发展。初步结果表明，这些想法有希望应用于几种蛋白质。完整开发的算法对于结构信息有限的系统特别有用，因为顺序参数可以单独使用基于序列信息的进化耦合残基来识别。该项目的具体目标包括：(1)进一步发展REAP方法，以有效探索与蛋白质功能相关的自由能景观；(2)测试新方法，以了解具有高生物学重要性但结构信息可用性有限的分子过程。主要研究底物转运的分子机制及其对以下转运过程的调控：(1)植物中硝酸盐通过根相关转运体的转运过程，在作物增产中的应用。(2) 5 -羟色胺在脑内的转运：通过人5 -羟色胺转运体阐明神经系统疾病的分子起源。(3)水稻SWEET家族转运蛋白转运糖及其在促进植株生长中的应用。尽管它们的功能和结构不同，但这些系统在调节模式方面有相似之处，这可以让我们全面了解膜转运体的调节机制。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。