Development & application of computational methods for the study of protein dynamics with PmHMGR as a model system

发展

• 批准号: 10607487
• 负责人: Mikaela Farrugia
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-25 至 2025-08-24
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607487
• 关键词: Acceleration; Benchmarking; Biochemical; Biological Models; Biology; Biophysics; Computational Technique; Computer Models; Computer Simulation; Computing Methodologies; Crystallography; Data; Development; Disease; Event; Functional disorder; Generations; Genetic; Goals; Health; Hybrids; Individual; Investigation; Methodology; Methods; Modeling; Molecular Conformation; Molecular Structure; Pathway interactions; Process; Protein Dynamics; Proteins; Reaction; Research; Research Personnel; Role; Sampling; Structure; Swarm intelligence; Techniques; Time; Universities; Validation; Weight; Work; X-Ray Crystallography; computer studies; cost; design; electron density; empowerment; enzyme mechanism; experimental study; falls; follow-up; high dimensionality; improved; insight; molecular dynamics; molecular mechanics; novel; particle; predictive modeling; protein complex; protein function; quantum; simulation; synergism; tool

PROJECT SUMMARY Diseases are frequently caused by dysfunction of proteins in the body, perhaps due to maladapted genetics or from a wide variety of other causes. Researchers can gain a glimpse into this function through the study of a protein’s mechanism and dynamics. Ideally, a complete understanding of the role of a protein in biophysical interactions would describe the entire mechanistic pathway on an atomistic and dynamic level. However, this cannot be attained with experimental studies alone with today’s capabilities. Computational studies can provide experimentally inaccessible quantitative and atomistic information so they serve as powerful tools for better understanding diseases and identifying targets for experimental follow-up and potential treatment, but they carry little weight without rigorous experimental validation. We seek to reconcile experimental and computational data, equipping researchers with a method to produce the aforementioned continuous and atomistic information on protein dynamics so that they can elucidate the long timescale dynamics of proteins on an atomic level. When deconvolving time-resolved crystallographic data, I will substitute the typical static crystallographic initial inputs with structures from molecular dynamics simulations and predictive models to improve the continuity and accuracy of deconvoluted data. The objective of this work is to produce the aforementioned ideal dynamics information for a significant portion of the mechanism of PmHMGR as a demonstration and refinement of the proposed Markov State informed Multilinear Singular Value Decomposition (MSiMSVD) method which reconciles experimental and computational data. Application of the MSiMSVD method to slow dynamical events, such as the PmHMGR 2nd hydride transfer, is limited by the ability of molecular dynamics to perform accurate long-timescale simulations. This often requires Transition State Force Fields (TSFFs), but their parameterization for biomolecules often falls into local optimization minima due to high dimensionality. To reduce local minima trapping and make TSFF generation more accessible for biophysical research, I will apply constraints and swarm intelligence techniques to improve current TSFF parameterization. Collectively, these aims will provide a means by which experimental and computational techniques can work synergistically to produce the continuous atomistic protein dynamics information ideal for the investigation of proteins and their related functions and diseases.

项目摘要 疾病通常是由体内蛋白质功能障碍引起的，也许 由于遗传学不良或来自多种其他原因。研究人员可以获得 通过研究蛋白质的机制和动力学来瞥见该功能。 理想情况下，完全了解蛋白质在生物物理相互作用中的作用将 在原子和动态水平上描述整个机械途径。然而， 这不能仅与当今能力相关的实验研究。 计算研究可以提供实验上无法访问的定量和原子 信息因此，它们是更好地理解疾病的强大工具， 确定实验随访和潜在治疗的目标，但它们的含量很少 我们试图调和实验性和 计算数据，为研究人员提供了一种产生理由的方法 关于蛋白质动力学的连续和原子信息，以便它们可以阐明 原子水平上蛋白质的长时间动力学。当撤离时间分辨时 晶体学数据，我将用典型的静态晶体学初始输入代替 分子动力学模拟和预测模型的结构，以改善 反价态数据的连续性和准确性。这项工作的目的是生产 关于很大一部分机制的理想动态信息 PMHMGR作为拟议的马尔可夫州的演示和完善 多线性奇异值分解（MSIMSVD）方法 实验和计算数据。将MSIMSVD方法应用于速度 动态事件，例如PMHMGR第二氢化物转移，受到受到的能力的限制 分子动力学进行准确的长时间模拟。这通常需要 过渡状态力场（TSFF），但其生物分子的参数经常 由于高维度，因此属于局部优化最小值。减少当地的最小值 捕获并使TSFF生成更容易进行生物物理研究，我将申请 约束和群智能技术，以改善当前的TSFF参数化。 总的来说，这些目标将提供一种实验和实验的手段 计算技术可以协同起作用以产生连续的原子 蛋白质动力学信息是研究蛋白质及其相关的理想 功能和疾病。