Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.

Autor:	de Jager M; Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands. Electronic address: m.e.dejager@uu.nl., Kolbeck PJ; Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany., Vanderlinden W; Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany; School of Physics and Astronomy, University of Edinburgh, Scotland, United Kingdom., Lipfert J; Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany., Filion L; Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands.
Jazyk:	angličtina
Zdroj:	Biophysical journal [Biophys J] 2024 Sep 17; Vol. 123 (18), pp. 3231-3241. Date of Electronic Publication: 2024 Jul 23.
DOI:	10.1016/j.bpj.2024.07.023
Abstrakt:	Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here, we present a simple and generic coarse-grained model for DNA-protein and protein-protein interactions and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand canonical ensemble, and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal-component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and protein-induced DNA compaction and provides a systematic and semi-quantitative approach for analyzing their mesoscale complexes. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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