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

项目摘要 蛋白质通常折叠成三维形状，并作为蜂窝计算机运行，定义明确 反应力学 - 因此，常见的生化表达“结构是功能”。但是，这个规则 仅适用于人类基因组的最慢的进化部分。进化更快的蛋白质是 通常有序的较少，既可以使他们的加速进化和扩展生化景观 哪种自然选择可以起作用。目前，几乎没有工具和概念框架来理解 快速发展的动态蛋白的序列功能关系。在整个生命之树中，生殖 蛋白质以特殊速率进化（通常比免疫基因快），而威尔本实验室研究了 动物特异性受精的生物物理学和分子进化。连续的进化 相互作用的精子和卵子蛋白已选择用于生化特性，例如内在疾病，弱 结合亲和力等使他们的研究复杂化。高场NMR光谱在结构中是独一无二的 它可以研究此类异质蛋白系统的方法，并且我对NMR进行了开创性的NMR研究 经典的受精研究模型（海洋鲍鱼）中的受精蛋白。在接下来的5年中，我们将 询问配子识别蛋白对规格特定重要的配子识别蛋白的序列功能关系 通过将高吞吐量诱变筛选与靶向生物物理分析配对以更好的施肥 了解分子动力学与蛋白质的发展性和相互作用动力学的复杂相互作用。 NMR 方法将扩展到哺乳动物施肥蛋白的研究。促进我们自己的工作， 增强其他研究人员的能力，基于进化基因组学的基于深度学习的分析工具，质谱法 将开发蛋白质组学和NMR动力学。拟议的研究将提供进化 框架更好地了解人类基因组编码的蛋白质生物化学的广度，并 包括潜水员培训本科生，研究生和博士后的机会