CAREER: Reinforcement Learning of the Free Energy Landscapes of Proteins

蛋白质是一种分子机器，在生物体中执行各种任务，如在细胞内和细胞之间运输营养物质和交流信号。其中一些蛋白质的构象或形状发生了变化，从而实现了这些功能。使用X射线结晶学等传统实验技术来可视化这些分子机器的不同形状是具有挑战性的。因此，对这些过程及其对蛋白质功能的影响的分子水平的理解仍然是难以捉摸的。由于缺乏分子信息，很难开发出调节蛋白质功能的工程方法。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，需要大量的计算机时间来观察蛋白质的长时间构象变化过程。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，了解蛋白质动力学在通过膜转运蛋白跨细胞膜运输分子中的作用。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疾病方面发挥着关键作用。与这些研究目标相一致，PI将开展推广活动，通过伊利诺伊大学的女孩数学、工程和科学冒险夏令营，向高中女孩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计划，让当地Urbana-Champaign学校的非裔美国男孩参与进来，向他们传授蛋白质结构和功能。该项目将开发能够有效探索与蛋白质构象变化相关的自由能景观的计算方法。这项工作是在这样一个假设的指导下进行的，即利用强化学习技术的思想，使用进化耦合残基对距离作为蛋白质功能动力学的序参数，将允许有效地采样自由能景观。“基于强化学习的自适应采样”(REAP)算法的开发已经启动。初步结果显示，这些想法在几种蛋白质上有很好的应用前景。完全开发的算法对于结构信息有限的系统将特别有用，因为仅基于序列信息就可以使用进化耦合残基来识别有序参数。该项目的具体目标包括：(1)进一步发展REAP方法学，以有效地探索与蛋白质功能相关的自由能景观；(2)测试新方法学，以了解具有高度生物学重要性但结构信息有限的分子过程。特别是，基质运输的分子机制及其对以下运输过程的调控将被研究：(1)植物通过与根相关的转运蛋白运输硝酸盐的过程，以及在提高作物产量方面的应用。(2)通过5-羟色胺转运体在脑内转运5-羟色胺，以阐明神经系统疾病的分子起源。(3)水稻甜味家族转运蛋白的糖运输及其在促进植物生长中的应用。尽管它们的功能和结构不同，但这些系统在监管模式方面有相似之处，可以全面了解膜转运体的监管机制。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.