English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

A litmus test for classifying recognition mechanisms of transiently binding proteins

MPS-Authors
/persons/resource/persons122009

Weikl,  Thomas R.
Thomas Weikl, Biomolekulare Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Article.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Chakrabarti, K. S., Olsson, S., Pratihar, S., Giller, K., Overkamp, K., Lee, K. O., et al. (2022). A litmus test for classifying recognition mechanisms of transiently binding proteins. Nature Communications, 13: 3792. doi:10.1038/s41467-022-31374-5.


Cite as: https://hdl.handle.net/21.11116/0000-000A-ACD9-0
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.