Chromatin image-driven modelling
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Chromatin image-driven modelling. / Kadlof, Michał; Banecki, Krzysztof; Chiliński, Mateusz; Plewczynski, Dariusz.
In: Methods, Vol. 226, 2024, p. 54-60.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Chromatin image-driven modelling
AU - Kadlof, Michał
AU - Banecki, Krzysztof
AU - Chiliński, Mateusz
AU - Plewczynski, Dariusz
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024
Y1 - 2024
N2 - The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.
AB - The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.
U2 - 10.1016/j.ymeth.2024.04.006
DO - 10.1016/j.ymeth.2024.04.006
M3 - Journal article
C2 - 38636797
AN - SCOPUS:85190426439
VL - 226
SP - 54
EP - 60
JO - Methods
JF - Methods
SN - 1046-2023
ER -
ID: 389422482