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Decoding Phase Separation of Prion-Like Domains through Data-Driven Scaling Laws

View ORCID ProfileM. Julia Maristany, Anne Aguirre Gonzalez, View ORCID ProfileJorge R. Espinosa, View ORCID ProfileJan Huertas, View ORCID ProfileRosana Collepardo-Guevara, View ORCID ProfileJerelle A. Joseph
doi: https://doi.org/10.1101/2023.06.14.543914
M. Julia Maristany
1Department of Physics, University of Cambridge, Cambridge, UK
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Anne Aguirre Gonzalez
2Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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Jorge R. Espinosa
3Universidad Complutense de Madrid, Madrid, Spain
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Jan Huertas
2Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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Rosana Collepardo-Guevara
2Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
4Department of Genetics, University of Cambridge, Cambridge, UK
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Jerelle A. Joseph
5Department of Chemical and Biological Engineering, Princeton University, USA
6Omenn–Darling Bioengineering Institute, Princeton University, USA
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  • For correspondence: [email protected]
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Abstract

Proteins containing prion-like low complexity domains (PLDs) are common drivers of the formation of biomolecular condensates and are prone to misregulation due to amino acid mutations. Here, we exploit the accuracy of our residue-resolution coarse-grained model, Mpipi, to quantify the impact of amino acid mutations on the stability of 140 PLD mutants from six proteins (hnRNPA1, TDP43, FUS, EWSR1, RBM14, and TIA1). Our simulations reveal the existence of scaling laws that quantify the range of change in the critical solution temperature of PLDs as a function of the number and type of amino acid sequence mutations. These rules are consistent with the physicochemical properties of the mutations and extend across the entire family tested, suggesting that scaling laws can be used as tools to predict changes in the stability of PLD condensates. Our work offers a quantitative lens into how the emergent behavior of PLD solutions varies in response to physicochemical changes of single PLD molecules.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The manuscript underwent revision to streamline the language and significantly shorten the technical computational sections. Minor revisions clarifying results and conducting finite size analysis are also included.

  • https://github.com/JuliMaristany/PLD_Scaling_Laws

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 24, 2024.
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Decoding Phase Separation of Prion-Like Domains through Data-Driven Scaling Laws
M. Julia Maristany, Anne Aguirre Gonzalez, Jorge R. Espinosa, Jan Huertas, Rosana Collepardo-Guevara, Jerelle A. Joseph
bioRxiv 2023.06.14.543914; doi: https://doi.org/10.1101/2023.06.14.543914
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Decoding Phase Separation of Prion-Like Domains through Data-Driven Scaling Laws
M. Julia Maristany, Anne Aguirre Gonzalez, Jorge R. Espinosa, Jan Huertas, Rosana Collepardo-Guevara, Jerelle A. Joseph
bioRxiv 2023.06.14.543914; doi: https://doi.org/10.1101/2023.06.14.543914

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