Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22634
Title: Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks
Authors: Motta, S
Pandini, A
Fornili, A
Bonati, L
Issue Date: 20-Mar-2021
Publisher: American Chemical Society (ACS)
Citation: Motta, S., Pandini, A., Fornili, A. and Bonati, L. (2021) 'Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks', Journal of Chemical Theory and Computation, 17(4), pp. 2080-2089. doi: 10.1021/acs.jctc.0c01308.
Abstract: © 2021 The Authors. 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.
URI: https://bura.brunel.ac.uk/handle/2438/22634
DOI: https://doi.org/10.1021/acs.jctc.0c01308
ISSN: 1549-9618
Other Identifiers: acs.jctc.0c01308
Appears in Collections:Dept of Computer Science Research Papers

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