A litmus test for classifying recognition mechanisms of transiently binding proteins

2022 | journal article. A publication with affiliation to the University of Göttingen.

Jump to: Cite & Linked | Documents & Media | Details | Version history

Cite this publication

​A litmus test for classifying recognition mechanisms of transiently binding proteins​
Chakrabarti, K. S.; Olsson, S.; Pratihar, S. ; Giller, K. ; Overkamp, K.; Lee, K. O. & Gapsys, V.  et al.​ (2022) 
Nature Communications13(1).​ DOI: https://doi.org/10.1038/s41467-022-31374-5 

Documents & Media

s41467-022-31374-5.pdf1.73 MBAdobe PDF

License

Published Version

Attribution 4.0 CC BY 4.0

Details

Authors
Chakrabarti, Kalyan S.; Olsson, Simon; Pratihar, Supriya ; Giller, Karin ; Overkamp, Kerstin; Lee, Ko On; Gapsys, Vytautas ; Ryu, Kyoung-Seok; de Groot, Bert L. ; Noé, Frank; Griesinger, Christian 
Abstract
Partner recognition in protein binding is critical for all biological functions, and yet, delineating its mechanism is challenging, especially when recognition happens within microseconds. We present a theoretical and experimental framework based on straight-forward nuclear magnetic resonance relaxation dispersion measurements to investigate protein binding mechanisms on sub-millisecond timescales, which are beyond the reach of standard rapid-mixing experiments. This framework predicts that conformational selection prevails on ubiquitin’s paradigmatic interaction with an SH3 (Src-homology 3) domain. By contrast, the SH3 domain recognizes ubiquitin in a two-state binding process. Subsequent molecular dynamics simulations and Markov state modeling reveal that the ubiquitin conformation selected for binding exhibits a characteristically extended C-terminus. Our framework is robust and expandable for implementation in other binding scenarios with the potential to show that conformational selection might be the design principle of the hubs in protein interaction networks.
Issue Date
2022
Journal
Nature Communications 
Organization
Max-Planck-Institut für Multidisziplinäre Naturwissenschaften 
eISSN
2041-1723
Language
English

Reference

Citations


Social Media