Multi-scale Modeling of Macromolecular Liquids, and Macromolecules in Solution

高分子液体和溶液中高分子的多尺度建模

• 批准号: 2154999
• 负责人: Marina Guenza
• 金额: $ 50万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154999&HistoricalAwards=false
• 关键词: Multi; scale; Modeling; Macromolecular; Liquids

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Marina Guenza of the University of Oregon will design multiscale modeling simulations for macromolecular systems that combine advanced coarse-grained models with atomistic simulations. The Guenza group will develop coarse-grained models of macromolecular liquids and macromolecules in solution. Synthetic macromolecular liquids will play an essential role in the transition from fossil carbon-based to renewable energy sources. An emerging and impelling societal goal is to design eco-friendly polymeric materials that maintain specific, desirable macroscopic properties. Biological macromolecules, i.e. proteins and nucleic acids are the building blocks of super-molecular machines that guide life processes. Modeling biomacromolecular behavior is expected to provide invaluable information and potential provide insight on biomacromolecular dysfunction and its relation to disease. By adopting multiscale modeling procedures, Guenza and coworkers will study how the global macromolecular properties depend on their local molecular structure. These studies cannot be completed by atomistic simulations alone. In-house computer codes will be shared with the scientific community through GitHub and dedicated websites. As a part of her broader impact, Guenza will further expand the student-led peer mediator program called DuckREFS that she has initiated in the Chemistry Department at the University of Oregon. This program trains graduate students to act as neutral, third-party resources for fellow peers in alleviating stress and resolving conflict. The students learn active listening, conflict negotiation, and dispute resolution.The first part of this project will develop the Integral Equation Theory of Coarse-Graining (IECG) by studying enthalpy, entropy, free energy, and isothermal compressibility, predicted by IECG across multiple resolutions. The implementation of temperature transferability of the IECG potential, and the extension of IECG to fine resolution using machine learning will enhance the applicability of the method. Atomistic and IECG simulation will be combined in an Adaptive Resolution Simulation (AdResS). The proposed work involves the scientific collaboration with Prof. Luigi delle Site at the Freie Universität in Berlin who spearheaded AdResS. The second part of this research project aims to further extend the Langevin Equation for Protein Dynamics (LE4PD). LE4PD is an efficient and accurate method to describe protein dynamics because it accounts for local conformational barriers, as well as the hydrophobic core, and hydrodynamic interactions. The proposed research will extend LE4PD by including new terms that couple protein fluctuations with rotation, translation, and viscous forces. The calculated coupling terms should facilitate the application of these methods to protein binding and to the formation of protein-DNA complexes.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.

在化学系化学理论、模型和计算方法(CTMC)计划的支持下，俄勒冈大学的玛丽娜·古扎将为大分子系统设计多尺度建模模拟，将先进的粗粒度模型与原子模拟相结合。古恩扎小组将开发大分子液体和溶液中大分子的粗粒度模型。合成高分子液体将在从化石碳基能源向可再生能源的过渡中发挥重要作用。一个新兴的和推动社会的目标是设计环保的聚合物材料，保持特定的，令人满意的宏观性能。生物大分子，即蛋白质和核酸，是指导生命过程的超分子机器的基石。生物大分子行为建模有望提供宝贵的信息，并有可能提供对生物大分子功能障碍及其与疾病的关系的洞察。通过采用多尺度建模程序，古萨和他的同事将研究全球大分子结构如何依赖于它们的局部分子结构。这些研究不能仅靠原子模拟来完成。内部计算机代码将通过GitHub和专用网站与科学界共享。作为她更广泛影响的一部分，古扎将进一步扩大她在俄勒冈大学化学系发起的学生主导的同伴调解人项目DuckREFS。该项目训练研究生在缓解压力和解决冲突方面为同行充当中立的第三方资源。学生学习主动倾听、冲突协商和争端解决。本项目的第一部分将通过研究热能、熵、自由能和等温可压缩性来发展粗粒化积分方程式理论，这些都是粗粒化在多个解决方案中预测的。实现了体心电势的温度传递，并利用机器学习将体心电势扩展到精细分辨率，将增强该方法的适用性。原子学和IECG模拟将在自适应分辨率模拟(ADRESS)中结合。拟议中的工作涉及与柏林自由大学的Luigi Delle Site教授的科学合作，后者是地址的带头人。本研究项目的第二部分旨在进一步推广蛋白质动力学的朗之万方程(LE4PD)。LE4PD是描述蛋白质动力学的一种有效而准确的方法，因为它考虑了局部构象障碍、疏水核心和流体动力学相互作用。这项拟议的研究将扩展LE4PD，包括将蛋白质波动与旋转、平移和粘性力相结合的新术语。计算出的耦合项应该有助于将这些方法应用于蛋白质结合和蛋白质-DNA复合体的形成。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。