Biological Liquid–Liquid Phase Separation, Biomolecular Condensates, and Membraneless Organelles: Now You See Me, Now You Don’t

Uversky, Vladimir N.

doi:10.3390/ijms241713150

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Preprint1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Advances in nuclear proteostasis of metazoans

Buggiani,

Meinnel,

Giglione

et al. 2024

Biochimie

View full text Add to dashboard Cite

Advances in nuclear proteostasis of metazoans

Buggiani,

Meinnel,

Giglione

et al. 2024

Biochimie

View full text Add to dashboard Cite

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

de Jager,

Kolbeck,

Vanderlinden

et al. 2024

Biophysical Journal

View full text Add to dashboard Cite

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

de Jager,

Kolbeck,

Vanderlinden

et al. 2024

Preprint

View full text Add to dashboard Cite

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 the 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 we 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 to protein-induced DNA compaction and provides a systematic and quantitative approach for analyzing their mesoscale complexes.

show abstract

Biological Liquid–Liquid Phase Separation, Biomolecular Condensates, and Membraneless Organelles: Now You See Me, Now You Don’t

Cited by 4 publications

References 35 publications

Advances in nuclear proteostasis of metazoans

Advances in nuclear proteostasis of metazoans

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

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

Contact Info

Product

Resources

About