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: 9979906
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979906
• 关键词: Amino Acid Sequence; Area; Biochemical; Biological Process; Biology; Biotechnology; Complex; DNA biosynthesis; DNA sequencing; Data; Data Set; Emerging Technologies; Enzymes; Genetic Diseases; Goals; Libraries; 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; statistical learning

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.

项目总结

项目成果

Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.

推断蛋白质序列功能与大规模阳性未标记的学习关系。

• DOI: 10.1016/j.cels.2020.10.007
• 发表时间: 2021-01-20
• 期刊: Cell systems
• 影响因子: 9.3
• 作者: Song H;Bremer BJ;Hinds EC;Raskutti G;Romero PA
• 通讯作者: Romero PA

Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation.

• DOI: 10.1038/s41420-021-00799-0
• 发表时间: 2022-01-10
• 期刊: Cell death discovery
• 影响因子: 7
• 作者: Roychowdhury H;Romero PA
• 通讯作者: Romero PA

Machine learning to navigate fitness landscapes for protein engineering.

• DOI: 10.1016/j.copbio.2022.102713
• 发表时间: 2022-06
• 期刊: Current opinion in biotechnology
• 影响因子: 7.7