Collaborative Research: A Control Theoretic Framework for Guided Folding and Unfolding of Protein Molecules

该基金将资助能够准确预测蛋白质折叠和展开途径的研究，并将其应用于计算机辅助抗病毒药物设计，基于蛋白质的纳米机器的控制以及与蛋白质错误折叠相关的疾病（如阿尔茨海默氏症）的治疗，从而促进科学进步，促进国家健康和繁荣。基于物理的方法可靠地捕获了控制蛋白质分子构象变化的过程，但通常需要大量的计算费用。最近开发的建模范例，它描述了蛋白质分子在大量的刚性纳米链接，折叠的影响下的原子间的力量，可以显着减少计算负担，但提出了挑战，确保预测的折叠和展开途径是现实的，而不是人为驱动的数值算法。在这个项目中，这一挑战是克服使用基于优化的控制理论框架来指导折叠和展开动力学，同时尊重生物现实的构象熵的变化率。从这个框架的开发中获得的知识将使蛋白质构象动力学的系统研究成为可能，包括冠状病毒刺突蛋白的解折叠途径，同时也推进了以前未开发的控制工具，这些工具可能有助于机器人在混乱的环境中导航。一种独特的蛋白质通路数据的声化方法将使这一知识广泛获得，并将整合到工程，计算机科学和艺术的本科生的课程项目中，以及旨在指导高中生STEM的研究活动。这项研究旨在弥合两个看似无关的优化领域-基于非线性控制和构象动力学的蛋白质，通过严格的开发和研究计算效率和数值稳定的算法，准确预测蛋白质折叠和展开，同时避免与人工快速构象熵的损失该项目将填补在使用动态顺应性方法编码熵损失约束方面知识的关键空白，方法是为具有大状态空间维度的蛋白质折叠动力学在超椭球体上开发一种新的非迭代、大规模、基于二次规划的控制方案;构建大规模、可变步长的数值积分算法，预计将减少积分步骤的数量，其中每一步都需要对非常大的原子间力矢量场进行繁重的计算;并开发了一种用于系统地研究蛋白质展开问题的控制理论方法。用于验证的地面实况数据将从全原子分子动力学模拟中获得，在模型蛋白质芽孢杆菌RNA酶的情况下，将从基于光学镊子的机械展开实验中获得公开的实验数据。该奖项反映了NSF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。

This grant will fund research that enables accurate prediction of pathways for protein folding and unfolding, with application to computer-aided anti-viral drug design, control of protein-based nano-machines, and treatment of diseases related to protein misfolding such as Alzheimer’s, thereby promoting the progress of science, and advancing the national health and prosperity. Physics-based approaches reliably capture the processes that govern conformational changes of protein molecules, but typically do so at great computational expense. A recently developed modeling paradigm, which describes protein molecules in terms of large numbers of rigid nano-linkages that fold under the influence of interatomic forces, can significantly reduce the computational burden, but presents challenges with ensuring that the predicted folding and unfolding pathways are realistic and not artificially driven by the numerical algorithm. In this project, this challenge is overcome using an optimization-based control theoretic framework to guide both folding and unfolding dynamics while respecting biologically realistic rates of change of conformational entropy. Knowledge gained from the development of this framework will enable systematic investigation of protein conformational dynamics, including unfolding pathways of coronavirus spike proteins, while also advancing previously unexplored control tools that may help robots navigate cluttered environments. A unique approach to sonification of protein pathway data will make this knowledge broadly accessible and will be integrated in course projects for undergraduate students in engineering, computer science, and art, as well as in research activities aiming to mentor high school students in STEM.This research aims to bridge the two seemingly unrelated fields of optimization-based nonlinear control and conformational dynamics of proteins through rigorous development and investigation of computationally efficient and numerically stable algorithms that accurately predict protein folding and unfolding while avoiding pathways associated with artificially rapid loss of conformational entropy. This project will fill the critical gap in knowledge of encoding entropy-loss constraints using the kinetostatic compliance method by developing a novel non-iterative, large-scale, quadratic programming-based control scheme over hyper-ellipsoids for protein folding dynamics with large state-space dimensions; constructing a large-scale, variable-step-size, numerical integration algorithm that is expected to reduce the number of integration steps, where each step requires the burdensome computation of a very large interatomic force vector field; and developing a control theoretic approach for systematically investigating the problem of protein unfolding. Ground truth data for validation will be obtained from all-atom molecular dynamics simulations and, in the case of the model protein barnase, publicly available experimental data from optical tweezer-based mechanical unfolding experiments.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.