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

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 an unprecedented set of 140 PLD mutants from six proteins (hnRNPA1, TDP43, FUS, EWSR1, RBM14, and TIA1). Our simulations reveal the existence of predictive rules 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. Remarkably, these rules are consistent with the physicochemical properties of the mutations and extend across the entire family tested, suggesting universal scaling laws govern PLD phase behaviour. Our work offers a quantitative lens into how the emergent behaviour of PLD solutions varies in response to physicochemical changes of single PLD molecules.