CAREER: Riemannian Reformulation of Collective Variable Based Free Energy Calculation Methods

职业：基于集体变量的自由能计算方法的黎曼重构

• 批准号: 1945465
• 负责人: Mahmoud Moradi
• 金额: $ 65万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945465&HistoricalAwards=false
• 关键词: CAREER; Riemannian; Reformulation; Collective; Variable

Mahmoud Moradi of University of Arkansas is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. Professor Moradi develops chemical theories that improve the accuracy of computational methods used for investigating the functions of biomolecules such as proteins at the molecular level. These theories specifically improve geometric models that describe how proteins change their shape and how such changes change the protein's behavior. Professor Moradi bridges the gap between the state-of-the-art computational methods and biomolecular applications by developing rigorous theories. Moradi and his research group pursue advanced geometric tools necessary to provide a robust molecular picture of protein changes. This research enables researchers to understanding biomolecular processes involved in protein function and leads to a better understanding of disease and a more efficient computational framework for drug design and discovery. By taking advantage of state-of-the-art supercomputers incorporating advanced computational methods, statistical physics techniques, and statistical analysis tools this research will quantify conformational changes of membrane proteins that will impact biological and biomedical sciences. This research lies at the intersection of Biology, Physics, Chemistry, Mathematics, Statistics, and Computer Science, and offers a firsthand experience in interdisciplinary science to students and trainees, in particular those underrepresented in science. The research outcomes also provide new materials for teaching at both the graduate and undergraduate level. The project equips the high school science teachers with a user-friendly molecular dynamics visualization platform to illustrate the dynamic nature of biomolecular processes to their students. In addition,interdisciplinary training opportunities are provided for undergraduate students from various departments who are interested in receiving short-term and long-term training in biomolecular simulations.Professor Moradi and his research group develop a Riemannian framework for free energy calculation methods for biomolecular simulations. Riemannian geometric tools are employed to both modify previously established non-Riemannian algorithms and design novel Riemannian algorithms aimed at describing protein dynamics. A detailed picture of such phenomena can currently be addressed using all-atom molecular dynamics simulations, that are often computationally intensive. These simulations cannot describe many biomolecular processes such as large-scale protein conformational changes due to their timescale differences. Although various enhanced sampling techniques have been developed over the past few decades to address this “timescale gap”, the application of these methods to biologically relevant systems remain challenging due to both computational costs and methodological flaws. Moradi and his research group address a molecular level characterization of conformational changes of membrane proteins using free energy calculation methods and path-finding algorithms within a Riemannian framework. Methodologies that are both robust and result in transition pathways and free energies that are invariant under coordinate transformations may result.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.

阿肯色州大学的Mahmoud Moradi得到了化学系化学理论、模型和计算方法项目的支持。Moradi教授开发了化学理论，提高了用于在分子水平上研究蛋白质等生物分子功能的计算方法的准确性。这些理论特别改进了描述蛋白质如何改变其形状以及这些变化如何改变蛋白质行为的几何模型。Moradi教授通过发展严格的理论，弥合了最先进的计算方法和生物分子应用之间的差距。Moradi和他的研究小组追求先进的几何工具，以提供蛋白质变化的强大分子图像。这项研究使研究人员能够了解蛋白质功能所涉及的生物分子过程，并导致更好地了解疾病和更有效的药物设计和发现的计算框架。通过利用最先进的超级计算机，结合先进的计算方法，统计物理技术和统计分析工具，这项研究将量化膜蛋白的构象变化，这将影响生物和生物医学科学。 这项研究位于生物学，物理学，化学，数学，统计学和计算机科学的交叉点，并为学生和学员提供跨学科科学的第一手经验，特别是那些在科学方面代表性不足的学生。研究成果还为研究生和本科生的教学提供了新的材料。该项目为高中科学教师提供了一个用户友好的分子动力学可视化平台，以向学生说明生物分子过程的动态性质。此外，还为有兴趣接受生物分子模拟短期和长期培训的各系本科生提供跨学科培训机会。Moradi教授及其研究小组开发了用于生物分子模拟的自由能计算方法的黎曼框架。黎曼几何工具都修改以前建立的非黎曼算法和设计新的黎曼算法，旨在描述蛋白质动力学。这种现象的详细情况目前可以使用全原子分子动力学模拟来解决，这通常是计算密集型的。这些模拟不能描述许多生物分子过程，如大规模的蛋白质构象变化，由于它们的时间尺度的差异。虽然在过去的几十年中已经开发了各种增强的采样技术来解决这个“时间尺度差距”，这些方法的应用，生物相关的系统仍然具有挑战性，由于计算成本和方法的缺陷。Moradi和他的研究小组在黎曼框架内使用自由能计算方法和寻路算法解决了膜蛋白构象变化的分子水平表征。该奖项反映了NSF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。

项目成果

Transient local secondary structure in the intrinsically disordered C-term of the Albino3 insertase

• DOI: 10.1016/j.bpj.2021.10.013
• 发表时间: 2021-11-16
• 期刊: BIOPHYSICAL JOURNAL
• 影响因子: 3.4
• 作者: Baucom, Dustin R.;Furr, Mercede;Heyes, Colin D.
• 通讯作者: Heyes, Colin D.

Binding affinity estimation from restrained umbrella sampling simulations.

• DOI: 10.1038/s43588-022-00389-9
• 发表时间: 2023-01
• 期刊: NATURE COMPUTATIONAL SCIENCE
• 作者: Kumar, Vivek Govind;Polasa, Adithya;Agrawal, Shilpi;Kumar, Thallapuranam Krishnaswamy Suresh;Moradi, Mahmoud
• 通讯作者: Moradi, Mahmoud

Molecular Dynamics–Based Thermodynamic and Kinetic Characterization of Membrane Protein Conformational Transitions

基于分子动力学的膜蛋白构象转变的热力学和动力学表征

• DOI: 10.1007/978-1-0716-1394-8_16
• 发表时间: 2021
• 期刊: Methods in molecular biology
• 作者: Ogden, Dylan;Moradi, Mahmoud
• 通讯作者: Moradi, Mahmoud

Mechanistic Picture for Chemomechanical Coupling in a Bacterial Proton-Coupled Oligopeptide Transporter from Streptococcus Thermophilus

• DOI: 10.1021/acs.jpcb.1c03982
• 发表时间: 2021-08-23
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY B
• 影响因子: 3.3
• 作者: Immadisetty, Kalyan;Moradi, Mahmoud
• 通讯作者: Moradi, Mahmoud