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

项目摘要 疾病通常是由体内蛋白质功能障碍引起的， 由于不适应的遗传或其他各种各样的原因。研究人员可以获得 通过对蛋白质的机制和动力学的研究来了解这种功能。 理想情况下，对蛋白质在生物物理相互作用中的作用的完整理解将 在原子和动态水平上描述整个机械路径。然而，在这方面， 这是以今天的能力，单靠实验研究所达不到的。 计算研究可以提供实验无法实现的定量和原子 信息，使它们成为更好地了解疾病的有力工具， 确定实验性随访和潜在治疗的目标，但它们几乎没有 没有严格的实验验证。我们试图调和实验和 计算数据，为研究人员提供了一种方法， 连续和原子信息的蛋白质动力学，使他们能够阐明 蛋白质在原子水平上的长时间动态。反卷积时间分辨时 晶体学数据，我将用典型的静态晶体学初始输入替换为 结构从分子动力学模拟和预测模型，以提高 解卷积数据的连续性和准确性。这项工作的目标是产生 上述理想的动力学信息的机制的重要部分， PmHMGR作为演示和细化马尔可夫状态的建议告知 多线性奇异值分解（MSiMSVD）方法， 实验和计算数据。MSiMSVD方法在慢信号处理中的应用 动力学事件，如PmHMGR第二次氢化物转移，是有限的能力， 分子动力学来进行精确的长时间模拟。这常常需要 过渡态力场（TSFF），但它们对生物分子的参数化通常 由于高维数，福尔斯陷入局部优化极小值。减少局部极小值 捕获，使TSFF一代更容易为生物物理研究，我将申请 约束和群体智能技术，以改善目前的TSFF参数化。 总的来说，这些目标将提供一种手段， 计算技术可以协同工作，以产生连续的原子 蛋白质动力学信息的理想研究蛋白质及其相关 功能和疾病。

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.