Develop light-oxygen-voltage (LOV) sensing optogenetics tools through novel computational approaches with experimental validation

通过经过实验验证的新颖计算方法开发光氧电压 (LOV) 传感光遗传学工具

• 批准号: 10661223
• 负责人: Peng Tao
• 金额: $ 44.45万
• 依托单位: SOUTHERN METHODIST UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661223
• 关键词: 3-Dimensional; Address; Award; Biological; Communication; Development; Dimensions; Genetic Engineering; Kinetics; Light; Machine Learning; Methods; Modeling; Molecular Conformation; Names; Natural regeneration; Optics; Oxygen; Procedures; Property; Protein Conformation; Proteins; Protocols documentation; Reaction; Regulation; Research; Research Activity; Sampling; Techniques; Tertiary Protein Structure; Theoretical model; Time; Training Activity; Validation; Wit; Work; autoencoder; biophysical properties; circadian pacemaker; computerized tools; deep learning; graduate student; improved; kinetic model; learning strategy; machine learning method; molecular dynamics; mutant; network models; novel; optogenetics; simulation; skills; tool; tool development; undergraduate student; voltage

SUMMARY Optogenetics is a powerful technique that integrates the use of light (optics) and genetic engineering. Light- oxygen-voltage (LOV) domains are light-responsive circadian clock regulation proteins and serve as a novel platform for the optogenetic tools development. Delineating allosteric mechanisms of various LOV domains is critical for such development. Molecular dynamics (MD) simulations are the main computational tools to reveal allosteric mechanisms as spatial-temporal information at the atomic level. However, there are two major road- blocks to the currently available MD simulation methods to elucidate LOV domain mechanisms: 1) limited time scale; 2) lack of kinetic information. Many enhanced sampling methods were developed to implicitly increase the accessible time scale of dynamics simulations, but are not suitable for simulations of protein allosteric mecha- nisms due to the requirement of constructing reaction coordinates a priori. To address this issue, we recently applied deep learning methods, named autoencoders, to develop novel dimensionality reduction models for al- losteric proteins. The main advantage of these models is the ability to accurately regenerate protein tertiary structure from the low dimensional space, a.k.a. latent space. There is also a lack of kinetics models for protein conformational changes underlying allostery. We developed a directed kinetic transition network (DKTN) model during the previous award period to model kinetics of protein conformational change based on MD simulations. Based on our recent work, in this application, we will continue to develop two new methods, the auto-encoded latent space (AELS) sampling methods and machine learning based directed kinetic transition network (ML- DKTN) methods, and apply these novel methods to elucidate the LOV domain mechanisms. With the experi- mental validation, we will further develop key mutants of the selected LOV domains as new optogenetic tools. We expect to develop a set of efficient computational tools to obtain allosteric function-related conformational ensembles and kinetics models. We will apply these tools to build conformational ensembles and kinetics models for key LOV domains proteins and their mutants to delineate their underlying allosteric mechanisms. These the- oretical models could provide direct guidance for the further development of optogenetic tools based on the selected LOV domains. The promising mutants identified in the proposed study will be subjected to experimental verification through biophysical characterization. The proposed research activities will also provide unique train- ing activities for motivated undergraduate and graduate students with various backgrounds to contribute to scientific research and improve their research, interpersonal, and communication skills.

概括 光遗传学是一种集成了光（光学）和基因工程的强大技术。光- 氧电压（LOV）结构域是光响应生物钟调节蛋白，可作为一种新型生物钟调节蛋白。 光遗传学工具开发平台。描述各种 LOV 域的变构机制是 对于这样的发展至关重要。分子动力学（MD）模拟是揭示分子动力学的主要计算工具 作为原子水平时空信息的变构机制。然而，有两条主要道路—— 目前可用的MD模拟方法来阐明LOV域机制：1）有限的时间 规模; 2）缺乏动力学信息。开发了许多增强采样方法来隐式增加 动力学模拟的可访问时间尺度，但不适合蛋白质变构机制的模拟 由于需要先验地构建反应坐标而导致的nisms。为了解决这个问题，我们最近 应用深度学习方法（称为自动编码器）来开发新颖的降维模型 洛斯特里克蛋白。这些模型的主要优点是能够准确地再生蛋白质三级 低维空间（又名潜在空间）的结构。还缺乏蛋白质的动力学模型 变构基础上的构象变化。我们开发了定向动力学转换网络（DKTN）模型 在上一个奖项期间，基于 MD 模拟来模拟蛋白质构象变化的动力学。 基于我们最近的工作，在这个应用程序中，我们将继续开发两种新方法，自动编码 潜在空间（AELS）采样方法和基于机器学习的定向动力学转移网络（ML- DKTN）方法，并应用这些新方法来阐明 LOV 域机制。随着经验 心理验证后，我们将进一步开发所选 LOV 结构域的关键突变体作为新的光遗传学工具。 我们期望开发一套高效的计算工具来获得变构功能相关的构象 系综和动力学模型。我们将应用这些工具来构建构象系综和动力学模型 用于关键 LOV 结构域蛋白及其突变体，以描述其潜在的变构机制。这些—— 理论模型可以为基于光遗传学工具的进一步开发提供直接指导 选定的 LOV 域。在拟议的研究中确定的有前途的突变体将接受实验 通过生物物理表征进行验证。拟议的研究活动还将提供独特的培训- 为具有不同背景、积极进取的本科生和研究生开展的活动 科学研究并提高他们的研究、人际交往和沟通技巧。