Detection of Protein–Protein Interactions Through Vesicle Targeting

Author:

Boysen Jacob H1,Fanning Saranna12,Newberg Justin3,Murphy Robert F3456,Mitchell Aaron P16

Affiliation:

1. Department of Microbiology and Institute of Cancer Research, Columbia University, New York, New York 10032

2. Department of Microbiology, University College Cork, Cork, Ireland

3. Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

4. Lane Center for Computational Biology and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

5. External Fellow, Freiburg Institute for Advanced Studies, University of Freiburg, 79104 Freiburg, Germany and

6. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Abstract

Abstract The detection of protein–protein interactions through two-hybrid assays has revolutionized our understanding of biology. The remarkable impact of two-hybrid assay platforms derives from their speed, simplicity, and broad applicability. Yet for many organisms, the need to express test proteins in Saccharomyces cerevisiae or Escherichia coli presents a substantial barrier because variations in codon specificity or bias may result in aberrant protein expression. In particular, nonstandard genetic codes are characteristic of several eukaryotic pathogens, for which there are currently no genetically based systems for detection of protein–protein interactions. We have developed a protein–protein interaction assay that is carried out in native host cells by using GFP as the only foreign protein moiety, thus circumventing these problems. We show that interaction can be detected between two protein pairs in both the model yeast S. cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence.

Publisher

Oxford University Press (OUP)

Subject

Genetics

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fluorescent toys ‘n’ tools lighting the way in fungal research;FEMS Microbiology Reviews;2021-02-17

2. References;Systems Immunology and Infection Microbiology;2021

3. Protein-Protein Interactions in Candida albicans;Frontiers in Microbiology;2019-08-07

4. A High-Throughput Candida albicans Two-Hybrid System;mSphere;2018-08-29

5. A Bimolecular Fluorescence Complementation Tool for Identification of Protein-Protein Interactions in Candida albicans;G3 Genes|Genomes|Genetics;2017-10-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3