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

基本信息

批准号：
2153901
负责人：
Mark Spong
金额：
$ 22.1万
依托单位：
University of Texas at Dallas
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=2153901&HistoricalAwards=false
关键词：
Collaborative Research Control Theoretic Framework

项目摘要

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.

这项赠款将资助研究，该研究能够准确预测蛋白质折叠和展开的途径，并应用于计算机辅助的抗病毒药物设计，控制蛋白质基于蛋白质的纳米机器以及治疗与蛋白质错误折叠有关的疾病，例如阿尔茨海默氏症的疾病，从而促进了科学和国家健康和繁荣的进步。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，但通常以巨大的计算费用进行。最近开发的建模范式，该范式用大量的刚性纳米链接来描述蛋白质分子，这些固定纳米链接在原子体间力的影响下折叠，可以显着降低计算伯嫩的挑战，但通过确保预测的折叠和不断发展的途径提出了挑战，并没有逼真的途径是逼真的，并且不逼真，并且由数值过时驱动。在这个项目中，使用基于优化的控制理论框架来克服这一挑战，以指导折叠和展开动态，同时尊重生物学上现实的构象熵变化速率。从该框架的开发中获得的知识将使蛋白质构象动力学的系统投资，包括冠状病毒尖峰蛋白的发展途径，同时还可以促进以前未开发的控制工具，这些工具可能有助于机器人导航杂乱的环境。一种独特的蛋白质途径数据索赔方法将使这些知识广泛访问，并将在工程，计算机科学和艺术领域的本科生以及研究活动中整合到课程项目中，旨在旨在通过STEM中的精神高中生进行精神上的高中学生。这项研究旨在桥接两个看似无关的基于优化的非线性控制和良好的蛋白质和辅助性的计算和辅助动态的，该领域的计算和辅助性的计算和辅助动态，并进行了计算的效率，并进行了良好的计算机，并进行了计算的效果，并进行了良好的计算动力学，并有效地进行了计算的动态。准确地预测蛋白质折叠和展开，同时避免与构象熵的人工快速丧失相关的途径。该项目将通过开发一种新型的非静态，大规模，二次编程的控制方案来填补使用动力学依从性方法来编码熵损失约束的知识的关键差距，该方案对具有较大状态空间维度的蛋白质折叠动力学而言；构建一种大规模的，可变大小的数值集成算法，该算法有望减少积分步骤的数量，其中每个步骤都需要对非常大的原子质互力矢量场进行朴素的计算；并开发一种控制理论方法，以系统地研究蛋白质的发展问题。将从全原子分子动力学模拟中获得基础真实数据，并且在模型蛋白barnase的情况下，将从基于光学镊子的机械外向实验中获得公开可用的实验数据。该奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务。