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

合作研究：蛋白质分子引导折叠和展开的控制理论框架

• 批准号: 2153744
• 负责人: Alireza Mohammadi
• 金额: $ 26.66万
• 依托单位: Regents of the University of Michigan - Dearborn
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153744&HistoricalAwards=false
• 关键词: Collaborative; Research; Control; Theoretic; Framework

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.

这笔赠款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（例如阿尔茨海默病）的治疗。促进科学进步，促进国民健康和繁荣，从而可靠地捕获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，这是最近开发的描述蛋白质分子的建模范例。就大而言在原子间力的影响下折叠的刚性纳米连接的数量可以显着减少计算负担，但在确保预测的折叠和展开路径是现实的而不是由数值算法人为驱动方面提出了挑战。使用基于优化的控制理论框架来指导折叠和展开动力学，同时尊重构象熵的生物学实际变化率，可以克服这一挑战。从该框架的开发中获得的知识将能够系统地研究蛋白质构象动力学，包括蛋白质构象的展开途径。冠状病毒尖峰蛋白质，同时还推进了以前未探索过的控制工具，可以帮助机器人在杂乱的环境中导航。一种独特的蛋白质途径数据的声音化方法将使这些知识能够被广泛获取，并将被整合到工程、计算机科学和艺术本科生的课程项目中。以及旨在指导 STEM 高中生的研究活动。这项研究旨在通过严格开发和研究计算高效且数值稳定的算法，在基于优化的非线性控制和蛋白质构象动力学这两个看似无关的领域之间架起桥梁。准确预测蛋白质折叠和展开，同时避免与人为快速构象熵损失相关的路径。该项目将通过开发一种新颖的非迭代、大规模、二次规划，填补使用动静态顺应性方法编码熵损失约束的知识空白。基于超椭球的控制方案，用于具有大状态空间维度的蛋白质折叠动力学，构建大规模、可变步长的数值积分算法，该算法有望减少积分步骤的数量，其中每个步骤都需要非常大的原子间力矢量场的繁重计算；以及开发一种控制理论方法，用于系统地研究蛋白质展开问题，以从全原子分子动力学模拟中获得用于验证的地面真实数据，对于模型蛋白质而言。 barnase，来自基于光镊的机械展开实验的公开实验数据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Wave space sonification of the folding pathways of protein molecules modeled as hyper-redundant robotic mechanisms

作为超冗余机器人机制建模的蛋白质分子折叠路径的波空间超声处理

• DOI: 10.1007/s11042-023-15385-y
• 发表时间: 2023-05
• 期刊: Multimedia Tools and Applications
• 影响因子: 3.6
• 作者: Kacem, Amal;Zbiss, Khalil;Watta, Paul;Mohammadi, Alireza
• 通讯作者: Mohammadi, Alireza

Chetaev Instability Framework for Kinetostatic Compliance-Based Protein Unfolding

基于动静态顺应性的蛋白质展开的 Chetaev 不稳定性框架

• DOI: 10.1109/lcsys.2022.3176433
• 发表时间: 2022-01
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Mohammadi, A.;Spong, Mark W.
• 通讯作者: Spong, Mark W.

Protein Molecules as Robotic Mechanisms: An Interdisciplinary Project-Based Learning Experience at the Intersection of Biochemistry and Robotics

蛋白质分子作为机器人机制：生物化学和机器人交叉学科的基于项目的跨学科学习体验

• 发表时间: 2023-06
• 期刊: 2023 ASEE Annual Conference & Exposition
• 作者: Mohammadi, Alireza;Heilman, Destin
• 通讯作者: Heilman, Destin

Prediction of Protein Folding Pathways under Entropy-Loss Constraints using Quadratic Programming-Based Nonlinear Control

使用基于二次规划的非线性控制预测熵损失约束下的蛋白质折叠途径

• 发表时间: 2023-06
• 期刊: 2023 American Control Conference (ACC
• 作者: Mohammadi, Alireza;Spong, Mark W.
• 通讯作者: Spong, Mark W.