Literature DB >> 27758101

Observing Extremely Weak Protein-Protein Interactions with Conventional Single-Molecule Fluorescence Microscopy.

Janghyun Yoo1,2,3, Tae-Sun Lee2,3, Byungsan Choi1,2,3, Min Ju Shon2,3, Tae-Young Yoon2,3.   

Abstract

Extremely weak protein-protein interactions (PPIs), signified by micromolar or even millimolar dissociation constants, are one of the keys to understanding the rapid responses of cellular systems. Although single-molecule methods are particularly useful in determining kinetics of biological processes, their application is largely limited to rather strong interactions because of the diffraction-limited observation volume. In this study, we report a single-molecule method that allows the characterization of PPIs using a prey concentration 4 orders of magnitude lower than the dissociation constant. Instead of increasing the concentration of diffusing molecules, which is inevitably limited by the optical diffraction limit, we employed an increased density of surface bait protein. The low occupancy of the surface baits permitted determination of the kinetics with single-molecule resolution. We used this approach to study a PPI network consisting of Ras and its downstream proteins including full-length Rafs and catalytic subunits of phosphoinositide 3-kinase.

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Year:  2016        PMID: 27758101     DOI: 10.1021/jacs.6b09542

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  4 in total

1.  Single-molecule calorimeter and free energy landscape.

Authors:  Yi Wang; Zhuodong Tang; Hong-Yuan Chen; Wei Wang; Nongjian Tao; Hui Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-08       Impact factor: 11.205

2.  Single-Molecule Protein Detection in a Biofluid Using a Quantitative Nanopore Sensor.

Authors:  Avinash Kumar Thakur; Liviu Movileanu
Journal:  ACS Sens       Date:  2019-08-21       Impact factor: 7.711

3.  Switching of Photocatalytic Tyrosine/Histidine Labeling and Application to Photocatalytic Proximity Labeling.

Authors:  Keita Nakane; Haruto Nagasawa; Chizu Fujimura; Eri Koyanagi; Shusuke Tomoshige; Minoru Ishikawa; Shinichi Sato
Journal:  Int J Mol Sci       Date:  2022-10-02       Impact factor: 6.208

4.  Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Authors:  Joseph M Cunningham; Grigoriy Koytiger; Peter K Sorger; Mohammed AlQuraishi
Journal:  Nat Methods       Date:  2020-01-06       Impact factor: 28.547

  4 in total

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