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Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

View ORCID ProfileThomas Sisk, View ORCID ProfilePaul Robustelli
doi: https://doi.org/10.1101/2023.07.21.550103
Thomas Sisk
Dartmouth College, Department of Chemistry, Hanover, NH, 03755
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Paul Robustelli
Dartmouth College, Department of Chemistry, Hanover, NH, 03755
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  • For correspondence: Paul.J.Robustelli@Dartmouth.edu
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Abstract

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long-time scale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We do not, however, find evidence for the existence of canonical conformational selection or induced fit binding pathways. We observe four kinetically separated native-like bound states that interconvert on time scales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or “fuzzy”, protein complex.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/paulrobustelli/Sisk_NTAIL_DeepMSM_2023

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 25, 2023.
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Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
Thomas Sisk, Paul Robustelli
bioRxiv 2023.07.21.550103; doi: https://doi.org/10.1101/2023.07.21.550103
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Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
Thomas Sisk, Paul Robustelli
bioRxiv 2023.07.21.550103; doi: https://doi.org/10.1101/2023.07.21.550103

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