A litmus test for classifying recognition mechanisms of transiently binding proteins
Artikel i vetenskaplig tidskrift, 2022
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.
Författare
Kalyan S. Chakrabarti
Max Planck Institute for Multidisciplinary Sciences
Krea University
Simon Olsson
Freie Universität Berlin
Chalmers, Data- och informationsteknik, Data Science och AI
Supriya Pratihar
Max Planck Institute for Multidisciplinary Sciences
Karin Giller
Max Planck Institute for Multidisciplinary Sciences
Kerstin Overkamp
Max Planck Institute for Multidisciplinary Sciences
Ko On Lee
Korea Basic Science Institute
Vytautas Gapsys
Max Planck Institute for Multidisciplinary Sciences
Kyoung Seok Ryu
Korea Basic Science Institute
Bert L. de Groot
Max Planck Institute for Multidisciplinary Sciences
Frank Noé
Freie Universität Berlin
Rice University
Stefan Becker
Max Planck Institute for Multidisciplinary Sciences
Donghan Lee
University of Louisville
Thomas R. Weikl
Max-Planck-Gesellschaft
Christian Griesinger
Max Planck Institute for Multidisciplinary Sciences
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 13 1 3792Ämneskategorier
Biofysik
Strukturbiologi
Bioinformatik och systembiologi
DOI
10.1038/s41467-022-31374-5
PubMed
35778416