Manuscript 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.
Funding
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 803326 to R.C.G.). MJM acknowledges the Winton Programme for Physics of Sustainability for doctoral funding. AAG is funded by the ERC (grant agreement No 803326). JRE acknowledges funding from the Ramon y Cajal fellowship (RYC2021-030937-I) and the Spanish National Agency for Research under the grant PID2022-136919NA-C33. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/X02332X/1 to JH] under the UK Research and Innovation (UKRI) Postdoctoral Fellowships Guarantee Scheme [project TF-CHROM-LLPS]. JAJ acknowledges research support from departmental start-up funds provided by the Department of Chemical and Biological Engineering and the Omenn--Darling Bioengineering Institute at Princeton University. JAJ also acknowledges research support from the Chan Zuckerberg Initiative DAF (an advised fund of Silicon Valley Community Foundation; grant 2023-332391) and the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM155259. This project made use of time on HPC granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim (http://hecbiosim.ac.uk), supported by EPSRC (grant no. EP/R029407/1).
