Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks.
dc.contributor.author | Motta, S | |
dc.contributor.author | Pandini, A | |
dc.contributor.author | Fornili, A | |
dc.contributor.author | Bonati, L | |
dc.date.accessioned | 2021-06-03T13:13:38Z | |
dc.date.available | 2021-06-03T13:13:38Z | |
dc.date.issued | 2021-03-29 | |
dc.identifier.citation | Motta, Stefano et al. "Reconstruction Of ARNT PAS-B Unfolding Pathways By Steered Molecular Dynamics And Artificial Neural Networks". Journal Of Chemical Theory And Computation, vol 17, no. 4, 2021, pp. 2080-2089. American Chemical Society (ACS), doi:10.1021/acs.jctc.0c01308. Accessed 3 June 2021. | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/72283 | |
dc.description.abstract | Several experimental studies indicated that large conformational changes, including partial domain unfolding, have a role in the functional mechanisms of the basic helix loop helix Per/ARNT/SIM (bHLH-PAS) transcription factors. Recently, single-molecule atomic force microscopy (AFM) revealed two distinct pathways for the mechanical unfolding of the ARNT PAS-B. In this work we used steered molecular dynamics simulations to gain new insights into this process at an atomistic level. To reconstruct and classify pathways sampled in multiple simulations, we designed an original approach based on the use of self-organizing maps (SOMs). This led us to identify two types of unfolding pathways for the ARNT PAS-B, which are in good agreement with the AFM findings. Analysis of average forces mapped on the SOM revealed a stable conformation of the PAS-B along one pathway, which represents a possible structural model for the intermediate state detected by AFM. The approach here proposed will facilitate the study of other signal transmission mechanisms involving the folding/unfolding of PAS domains. | en_US |
dc.language | eng | |
dc.publisher | American Chemical Society | en_US |
dc.relation.ispartof | J Chem Theory Comput | |
dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks. | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021 The Authors. Published by | |
dc.identifier.doi | 10.1021/acs.jctc.0c01308 | |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/33780250 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
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