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

项目摘要 蛋白质通常折叠成三维形状，并作为细胞机器运作， 反应力学--因此，生物化学的常用表达是“结构即功能”。然而，这一规则 只适用于人类基因组中进化最慢的部分。进化得更快的蛋白质 通常不太有序，这既使它们能够加速进化，又扩大了生物化学景观， 自然选择可能起的作用。目前，只有很少的工具和概念框架来理解 快速进化的动态蛋白质的序列-功能关系。穿过生命之树， 蛋白质以惊人的速度进化-通常比免疫基因更快-Wilburn实验室研究了 生物物理学和动物物种特异性受精的分子进化。持续的共同进化 相互作用的精子和卵子蛋白质选择了生化特性，如内在的障碍，弱 结合亲和力等使其研究复杂化。高场核磁共振光谱是独特的结构 方法的能力，研究这种异质蛋白质系统，我开创了核磁共振研究， 受精研究的经典模型中的受精蛋白（海洋鲍鱼）。未来5年，我们将 询问配子识别蛋白质的序列与功能的关系， 通过将高通量诱变筛选与靶向生物物理分析配对， 了解分子动力学与蛋白质进化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受精蛋白的研究。为了方便我们自己的工作， 赋予其他研究人员，进化基因组学，质谱分析中基于深度学习的分析工具 蛋白质组学和核磁共振动力学将得到发展。这项研究将提供一种进化的 框架，以更好地了解由人类基因组编码的蛋白质生物化学的广度， 包括为本科生、研究生和博士后提供的各种培训机会

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