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

职业：通过实验引导模拟进行肽自组装的多尺度建模

• 批准号: 1751471
• 负责人: Andrew White
• 金额: $ 50万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751471&HistoricalAwards=false
• 关键词: CAREER; Multiscale; Modeling; Peptide; Self

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.

PI：白色，Andrew D.提案编号：1751471机构：罗切斯特大学标题：职业：多尺度建模肽自组装与实验导向模拟自组装是自发组织成一个超分子复合物或新的相分子没有形成化学键。该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。肽自组装的建模已经被材料科学和结构生物学中的应用所驱动。在结构生物学中，研究的动机是许多引起疾病的肽，它们自组装成有毒结构。该建议的主要目标是开发一个多尺度建模的肽组装，其中分子模拟使用实验数据进行校正的框架。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颖能力相结合。自组装是分子建模和模拟中最具挑战性的问题之一，因为它跨越多个长度尺度和通常多个时间尺度。为了模拟更大尺度的相互作用而将原子分组在一起的粗粒度技术与参考实验的一致性较差，并且缺乏纠正这些差异的严格理论。该提案旨在通过最小限度地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。 所提出的新方法得到了初步数据的支持，这些数据表明准确性和效率得到了提高。这些改进允许模拟大量不同的系统，并保证与拟议的平行实验室实验的一致性。保真度和模拟数量的增加可能会导致深度学习模型的发展，这将允许重新设计自组装结构。该提案的主要目的是使用这种偏置技术沿着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β肽，其是导致阿尔茨海默病的毒性剂。拟议研究的总体科学目标是通过使用计算机模拟更好地了解熵，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涉及基于网络的应用程序的开发，用于教授本科生关于粗粒度，实验导向的模拟，和自大会和虚拟现实研讨会，参加罗切斯特大学的卡恩斯中心导师计划的高中生。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识产权评估的支持。优点和更广泛的影响审查标准。

项目成果

HOOMD-TF: GPU-Accelerated, Online Machine Learning in the HOOMD-blue Molecular Dynamics Engine

HOOMD-TF：HOOMD-blue 分子动力学引擎中的 GPU 加速在线机器学习

• DOI: 10.21105/joss.02367
• 发表时间: 2020
• 期刊: Journal of Open Source Software
• 作者: Barrett, Rainier;Chakraborty, Maghesree;Amirkulova, Dilnoza;Gandhi, Heta;Wellawatte, Geemi;White, Andrew
• 通讯作者: White, Andrew

Combining enhanced sampling with experiment-directed simulation of the GYG peptide

• DOI: 10.1142/s0219633618400072
• 发表时间: 2018-05-01
• 期刊: JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY
• 影响因子: 2.4
• 作者: Amirkulova, Dilnoza B.;White, Andrew D.
• 通讯作者: White, Andrew D.

Real-Time Interactive Simulation and Visualization of Organic Molecules

• DOI: 10.1021/acs.jchemed.9b01161
• 发表时间: 2020-11-10
• 期刊: JOURNAL OF CHEMICAL EDUCATION
• 影响因子: 3
• 作者: Gandhi, Heta A.;Jakymiw, Sebastian;White, Andrew D.
• 通讯作者: White, Andrew D.

Recent advances in maximum entropy biasing techniques for molecular dynamics

• DOI: 10.1080/08927022.2019.1608988
• 发表时间: 2019-10-13
• 期刊: MOLECULAR SIMULATION
• 影响因子: 2.1
• 作者: Amirkulova, D. B.;White, A. D.
• 通讯作者: White, A. D.

Iterative symbolic regression for learning transport equations

• DOI: 10.1002/aic.17695
• 发表时间: 2022-03-31
• 期刊: AICHE JOURNAL
• 影响因子: 3.7
• 作者: Ansari, Mehrad;Gandhi, Heta A.;White, Andrew D.
• 通讯作者: White, Andrew D.