DMS/NIGMS 2: Integrated Analysis of Fusion Protein Conformational Changes for Virus Entry

DMS/NIGMS 2：病毒进入融合蛋白构象变化的综合分析

• 批准号: 10794657
• 负责人: Jin Liu
• 金额: $ 27.87万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794657
• 关键词: 2019-nCoV; Acceleration; Address; Biological; Biological Process; COVID-19 pandemic; Cell membrane; Cells; Chimeric Proteins; Cholesterol; Complex; Computer Models; Computer Simulation; Dengue; Development; Dimensions; Endocytosis; Event; Exocytosis; Freedom; Future; Grain; HIV; Health; Herpesviridae; Human; Infection; Lipids; Liquid substance; Machine Learning; Measurement; Membrane; Membrane Fluidity; Membrane Fusion; Methods; Modeling; Molecular; Molecular Conformation; Monoclonal Antibodies; Morbidity - disease rate; National Institute of General Medical Sciences; Organism; Pathway interactions; Process; Protein Conformation; Proteins; Reaction; Research; Role; Sampling; Scheme; Simplexvirus; Solid; Spanish flu; Structure; Surface; Techniques; Variant; Viral; Viral Fusion Proteins; Virion; Virus; Virus Diseases; effective intervention; env Gene Products; experience; experimental study; flexibility; innovation; insight; machine learning algorithm; machine learning method; membrane model; molecular modeling; mortality; multi-scale modeling; multidisciplinary; mutant; nanosized; neural network; novel; novel coronavirus; obligate intracellular parasite; pathogen; simulation; sperm cell; zygote

Virus infections remain major threats to human health worldwide as demonstrated by COVID-19 pandemic caused by SARS-CoV-2 and its variants. All enveloped viruses must fuse with host membranes to initiate the infection process. Membrane fusion is a critical, but poorly understood biological process that is driven by protein conformational changes. Membrane fusion is a highly complex, multistage and multiscale process, which is difficult to investigate through scale-specific techniques (both experimentally and numerically). In this project, we propose to investigate the structural changes of fusion proteins and virus fusion through a combination of multiscale modeling, machine learning, and complementary experimentation. Because of our decades long experience in working with the herpes simplex virus (HSV), we will utilize the herpesvirus fusogen, gB, a class Ill fusion protein, as a model protein, to elucidate protein conformational changes during virus fusion. The specific research aims are: (1) To delineate the conformational changes of viral fusion proteins, through development of the machine learning facilitated enhanced sampling scheme for fusion proteins and delineation of the sequential conformation changes of gB protein for HSV fusion by a combination of machine learning and experiments; (2) To elucidate membrane fusion driven by viral fusion protein conformational changes, through development of a multiscale model for membrane fusion, assessment of the role of membrane fluidity in fusion and elucidation of the importance of the gB membrane proximal region on gB conformational changes and membrane fusion through combined simulations and experiments. This research will establish experimentally validated, powerful modeling platforms for exploration of the protein conformational changes and will bridge the multiple spatial and temporal scales involved in the fusion process. The machine learning method for enhanced sampling is highly innovative and crucial to capture the large-scale structural (conformational) changes and associated energy profiles of fusion proteins, and to identify the appropriate pathways during the fusion process. The integration of the gradient-based optimization method to the machine learning algorithm is novel to identify the most appropriate reaction coordinates.

正如COVID-19所证明的那样，病毒感染仍然是全球人类健康的主要威胁 由SARS-CoV-2及其变种引起的大流行。所有有包膜病毒必须与宿主细胞膜融合 启动感染过程膜融合是一个关键的，但知之甚少的生物过程 这是由蛋白质构象变化驱动的。膜融合是一个高度复杂、多阶段且 多尺度过程，这是很难通过特定尺度的技术研究（无论是实验 数字）。在这个项目中，我们打算研究融合蛋白的结构变化， 通过多尺度建模、机器学习和互补的组合， 实验因为我们几十年来在单纯疱疹病毒方面的工作经验 (HSV)我们将利用疱疹病毒融合蛋白gB作为模型蛋白， 阐明病毒融合过程中蛋白质构象的变化。具体的研究目的是：（1） 描述病毒融合蛋白的构象变化，通过机器的开发， 学习促进了融合蛋白的增强采样方案和序列的描绘 通过机器学习和生物信息学的结合， 实验;（2）为了阐明由病毒融合蛋白构象变化驱动的膜融合， 通过开发膜融合的多尺度模型，评估膜的作用， 融合的流动性和阐明gB膜近端区域对gB的重要性 构象变化和膜融合。 这项研究将建立实验验证，强大的建模平台，探索 蛋白质构象的变化，并将桥梁多个空间和时间尺度参与 聚变过程用于增强采样的机器学习方法具有高度创新性，对于 捕捉大规模的结构（构象）变化和相关的能量分布融合 蛋白质，并在融合过程中确定适当的途径。的整合 基于梯度优化的方法是机器学习中最新颖的识别算法 合适的反应坐标。