Karamanos, TK orcid.org/0000-0003-2297-540X, Kalverda, AP and Radford, SE orcid.org/0000-0002-3079-8039 (2022) Generating Ensembles of Dynamic Misfolding Proteins. Frontiers in Neuroscience, 16. 881534. ISSN 1662-453X
Abstract
The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β2-microgloblulin (β2m) can be captured and investigated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Karamanos, Kalverda and Radford. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | ensemble calculations, protein misfolding, machine learning, intrinsic disorder, oligomerization, NMR spectroscopy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > NMR (Leeds) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > Structural Molecular Biology (Leeds) |
Funding Information: | Funder Grant number Wellcome Trust 223268/Z/21/Z |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2022 14:17 |
Last Modified: | 07 Jun 2022 13:25 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/fnins.2022.881534 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184513 |