Integration of biophysics and deep learning to understand species-specificity of fertilization and the rapid evolution of protein disorder

整合生物物理学和深度学习来了解受精的物种特异性和蛋白质紊乱的快速进化

• 批准号: 10714030
• 负责人: Damien Beau Wilburn
• 金额: $ 37.58万
• 依托单位: OHIO 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: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714030
• 关键词: 3-Dimensional; Acceleration; Adopted; Affinity; Animals; Binding; Biochemical; Biological Assay; Biophysics; Cell physiology; Complex; Development; Disease; Egg Proteins; Evolution; Fertilization; Genes; Genomics; Germ Cells; Goals; Human Genome; Immune; Kinetics; Life; Mass Spectrum Analysis; Mechanics; Methods; Modeling; Molecular Evolution; Mutagenesis; NMR Spectroscopy; Natural Selections; Postdoctoral Fellow; Property; Protein Biochemistry; Protein Dynamics; Proteins; Proteomics; Reaction; Research; Research Personnel; Shapes; Species Specificity; Structure; System; Trees; Work; abalone; analytical tool; biophysical analysis; deep learning; deep learning model; empowerment; functional plasticity; graduate student; marine; molecular dynamics; molecular shape; reproductive; sperm protein; tool; training opportunity; undergraduate student

Project Summary Proteins often fold into three-dimensional shapes and operate as cellular machines with well-defined reaction mechanics – hence the common biochemical expression “structure is function.” However, this rule only applies to the slowest evolving portions of the human genome. Proteins that evolve more rapidly are typically less ordered, both enabling their accelerated evolution and expanding the biochemical landscape on which natural selection may act. Currently there exist few tools and conceptual frameworks to understand the sequence-function relationship of quickly evolving dynamical proteins. Across the tree of life, reproductive proteins evolve at extraordinary rates – typically faster than immune genes – and the Wilburn lab studies the biophysics and molecular evolution of species-specific fertilization in animals. The continuous coevolution of interacting sperm and egg proteins has selected for biochemical properties such as intrinsic disorder, weak binding affinities, etc. that complicate their study. High-field NMR spectroscopy is unique among structural methods in its ability to study such heterogenous protein systems, and I have pioneered NMR studies of fertilization proteins in a classic model of fertilization research (marine abalone). Over the next 5 years, we will interrogate the sequence-to-function relationships of gamete recognition proteins important for species-specific fertilization by pairing high throughput mutagenesis screens with targeted biophysical analyses to better understand the complex interplay of molecular dynamics with protein evolvability and interaction kinetics. NMR methods will be expanded to the study of mammalian fertilization proteins. To facilitate our own work and empower other researchers, deep learning-based analytical tools in evolutionary genomics, mass spectrometry proteomics, and NMR dynamics will be developed. The proposed research will provide an evolutionary framework to better understand the breadth of protein biochemistry encoded by the human genome, and includes diverse training opportunities for undergraduate, graduate students, and postdocs

项目概要 蛋白质通常折叠成三维形状，并像具有明确定义的细胞机器一样运行 反应力学——因此常见的生化表达“结构就是功能”。 仅适用于人类基因组中进化最慢的部分，而进化较快的蛋白质是。 通常有序性较低，这既促进了它们的加速进化，又扩大了生物化学景观 目前，几乎没有工具和概念框架可以理解自然选择的作用。 快速进化的动态蛋白质的序列功能关系跨越生命之树，生殖。 蛋白质以惊人的速度进化——通常比免疫基因更快——威尔本实验室研究了 动物物种特异性受精的生物物理学和分子进化。 相互作用的精子和卵子蛋白质选择了生化特性，例如内在紊乱、弱 结合亲和力等使他们的研究变得复杂，高场核磁共振波谱在结构中是独一无二的。 方法在其研究这种异质蛋白质系统的能力中，我开创了核磁共振研究 受精研究经典模型（海洋鲍鱼）中的受精蛋白。在接下来的 5 年里，我们将。 询问对物种特异性很重要的配子识别蛋白的序列与功能关系 通过将高通量诱变筛选与有针对性的生物物理分析相结合来更好地进行受精 了解分子动力学与蛋白质进化性和相互作用动力学的复杂相互作用。 方法将扩展到哺乳动物受精蛋白的研究，以促进我们自己的工作和 为其他研究人员提供基于深度学习的进化基因组学分析工具、质谱分析工具 蛋白质组学和核磁共振动力学将得到发展，拟议的研究将提供进化。 框架以更好地了解人类基因组编码的蛋白质生物化学的广度，以及 包括为本科生、研究生和博士后提供多样化的培训机会