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

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 observe...

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Autores principales: Sisk, Thomas, Robustelli, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401938/
https://www.ncbi.nlm.nih.gov/pubmed/37546728
http://dx.doi.org/10.1101/2023.07.21.550103
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author Sisk, Thomas
Robustelli, Paul
author_facet Sisk, Thomas
Robustelli, Paul
author_sort Sisk, Thomas
collection PubMed
description 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 N(TAIL) 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 N(TAIL). 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 N(TAIL) 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.
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spelling pubmed-104019382023-08-05 Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model Sisk, Thomas Robustelli, Paul bioRxiv Article 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 N(TAIL) 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 N(TAIL). 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 N(TAIL) 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. Cold Spring Harbor Laboratory 2023-07-25 /pmc/articles/PMC10401938/ /pubmed/37546728 http://dx.doi.org/10.1101/2023.07.21.550103 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Sisk, Thomas
Robustelli, Paul
Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title_full Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title_fullStr Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title_full_unstemmed Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title_short Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model
title_sort folding-upon-binding pathways of an intrinsically disordered protein from a deep markov state model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401938/
https://www.ncbi.nlm.nih.gov/pubmed/37546728
http://dx.doi.org/10.1101/2023.07.21.550103
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