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.

项目概要 疾病常常是由体内蛋白质功能障碍引起的，也许 由于基因适应不良或各种其他原因，研究人员可能会获益。 通过研究蛋白质的机制和动力学来了解这一功能。 理想情况下，全面了解蛋白质在生物物理相互作用中的作用将 在原子和动态层面上描述整个机制路径。 以今天的能力，仅靠实验研究是无法实现这一目标的。 计算研究可以提供实验上无法获得的定量和原子性 信息，因此它们可以成为更好地了解疾病和 确定实验后续和潜在治疗的目标，但它们几乎没有什么意义 我们寻求协调实验和未经严格实验验证的重量。 计算数据，为研究人员提供一种进行讨论的方法 有关蛋白质动力学的连续和原子信息，以便他们能够阐明 当解卷积时间分辨时蛋白质的长时标动力学。 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，以改进 这项工作的目标是产生解卷积数据的连续性和准确性。 机制的重要部分的理想动力学信息 PmHMGR 作为建议的马尔可夫状态的演示和改进 协调的多线性奇异值分解 (MSiMSVD) 方法 实验和计算数据的MSiMSVD方法的应用。 动力学事件，例如 PmHMGR 第二次氢化物转移，受到以下能力的限制 分子动力学来执行精确的长时间尺度模拟。 过渡态力场 (TSFF)，但它们对生物分子的参数化通常 由于高维而陷入局部优化最小值，以减少局部最小值。 捕获并使 TSFF 生成更容易用于生物物理研究，我将申请 约束和群体智能技术来改进当前 TSFF 参数化。 总的来说，这些目标将提供一种手段，通过这些手段进行实验和 计算技术可以协同工作以产生连续的原子论 蛋白质动力学信息是蛋白质及其相关研究的理想选择 功能和疾病。