Data-driven analysis of protein structure, function, and regulation

蛋白质结构、功能和调控的数据驱动分析

• 批准号: 9318542
• 负责人: Philip Anthony Romero
• 金额: $ 38.25万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9318542
• 关键词: Amino Acid Sequence; Area; Biochemical; Biological Process; Biology; Biotechnology; Complex; DNA biosynthesis; DNA sequencing; Data; Data Set; Emerging Technologies; Enzymes; Goals; Hereditary Disease; Libraries; Machine Learning; Medicine; Membrane Transport Proteins; Methods; Microfluidics; Molecular; Play; Property; Protein Analysis; Proteins; Regulation; Role; Symptoms; Work; design; genetic disorder diagnosis; novel diagnostics; novel therapeutics; protein function; protein structure function; screening

PROJECT SUMMARY Proteins are capable of performing a wide variety of complex molecular functions and play a central role in all biological processes. A detailed and quantitative understanding of the relationship between a protein's sequence and it's biochemical properties would have a profound impact across all areas of biology, medicine, and biotechnology. We are developing data-driven approaches for dissecting the molecular basis of protein function. Our general framework involves designing informative libraries of protein sequences, experimentally mapping the relationship between sequence and function, and extracting detailed functional information from large sequence-function data sets. This work leverages emerging technologies and methods in DNA sequencing and synthesis, microfluidic screening, large-scale statistical learning, and optimization. We will develop generalizable platforms that can be applied to study a wide variety of enzymes and membrane transport proteins.

项目概要 蛋白质能够执行多种复杂的分子功能，并在所有生命活动中发挥核心作用 生物过程。对蛋白质之间关系的详细和定量理解 序列及其生化特性将对生物学、医学、 和生物技术。我们正在开发数据驱动的方法来剖析蛋白质的分子基础 功能。我们的总体框架涉及通过实验设计信息丰富的蛋白质序列库 映射序列和功能之间的关系，并从中提取详细的功能信息 大型序列函数数据集。这项工作利用了 DNA 领域的新兴技术和方法 测序和合成、微流体筛选、大规模统计学习和优化。我们将 开发可用于研究各种酶和膜的通用平台 运输蛋白质。