CAREER: Multiscale Modeling of Peptide Self-Assembly with Experiment Directed Simulation

PI: White, Andrew D.提案编号：1751471机构：罗切斯特大学标题：职业：多肽自组装与实验导向模拟的多尺度建模自组装是分子自发组织成超分子复合物或新相而不形成化学键。这个过程描述了从蛋白质折叠到液晶取向到聚合物纳米复合材料形成的广泛现象。多肽自组装的建模已经被材料科学和结构生物学的应用所驱动。在结构生物学中，研究的动机是许多自组装成有毒结构的致病肽。本提案的主要目标是开发一个多肽组装的多尺度建模框架，其中分子模拟使用实验数据进行校正。该研究将最先进的计算机模拟技术与使用实验数据作为模拟的额外输入以提高其准确性的新能力相结合。自组装是分子建模和模拟中最具挑战性的问题之一，因为它跨越了多个长度尺度和多个时间尺度。将原子聚集在一起以模拟大尺度相互作用的粗粒技术与参考实验的一致性较差，并且缺乏严格的理论来纠正这些差异。本提案旨在通过最小化偏置模拟来纠正模拟预测与实验数据之间的差异来解决这些缺点。初步数据表明，提出的新方法提高了准确性和效率。这些改进允许大量不同的系统进行模拟，并保证与提出的并行实验室实验的一致性。保真度和仿真数量的增加可能会导致深度学习模型的发展，从而允许重新设计自组装结构。该提案的主要目的是利用这种偏倚技术以及已建立的多尺度模拟方法来研究多种自组装肽，特别强调淀粉样蛋白肽是导致阿尔茨海默病的有毒物质。这项拟议研究的总体科学目标是通过使用计算机模拟来更好地理解熵、分子结构和自组装之间相互作用的分子细节。研究和教育的整合将包括开发基于网络的应用程序，用于教授本科生关于粗粒度、实验导向模拟和自组装的知识，并为参加罗切斯特大学卡恩斯中心指导计划的高中生提供虚拟现实研讨会。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。

PI: White, Andrew D. Proposal Number: 1751471 Institution: University of RochesterTitle: CAREER: Multiscale Modeling of Peptide Self-Assembly with Experiment Directed SimulationSelf-assembly is the spontaneous organization of molecules into a supramolecular complex or new phase without the formation of chemical bonds. This process describes a wide range of phenomena from protein folding, to liquid crystal orientation, to polymer nanocomposite formation. Modeling of peptide self-assembly has been driven by applications in materials science and structural biology. In structural biology, research is motivated by the many disease causing peptides that self-assemble into toxic structures. The main objective of this proposal is the development of a framework for multiscale modeling of peptide assembly, in which molecular simulations are corrected using experimental data. The proposed research combines state-of-the art computer simulation techniques with the novel capability of using experimental data as an extra input to simulations to improve their accuracy.Self-assembly is one of the most challenging problems for molecular modeling and simulation because it spans multiple length-scales and often multiple time-scales. Coarse-grain techniques which group atoms together in order to simulate larger-length scale interactions suffer from poor agreement with reference experiments and lack rigorous theory for correcting these discrepancies. This proposal aims to address these shortcomings by minimally biasing simulations to correct discrepancies between simulation predictions and experimental data. The proposed new methodology is supported by preliminary data demonstrating improved accuracy and efficiency. These improvements allow a large number of distinct systems to be simulated and guarantee consistency with proposed parallel laboratory experiments. The increase in fidelity and simulation number may lead to the development of deep-learning models that will allow de novo design of self-assembling structures. The main objective of the proposal is to use this biasing technique along with established multiscale simulation methods to study multiple self-assembling peptides, with a particular emphasis on Amyloid beta peptide which is the toxic agent responsible for Alzheimer's disease. The overall scientific objective of the proposed research is to better understand the molecular details of the interplay between entropy, molecular structure and self-assembly through the use of computer simulations. Integration of research and education will involve the development of web-based applications for teaching undergraduate students about coarse-graining, experiment-directed simulation, and self-assembly and a virtual reality workshop to high school students participating in the University of Rochester's Kearns Center mentorship program.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.