Rob Scrutton

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.

Electrochemical gradients are essential to the functioning of cells and are typically formed across membranes using active transporters and require energy input to maintain them. Here, we show by contrast that biomolecular condensates are able to sustain significant pH gradients without any external energy input. We explore the thermodynamic driving forces that establish this gradient using a microfluidics-based droplet platform that allows us to sample in a continuous manner both the stability and composition of the condensates across a wide pH range. These results reveal that condensed biomolecular systems adjust the pH of the dense phase towards the isoelectric point (pI) of the component polypeptide chains. We demonstrate, on the basis of two representative systems, FUS and PGL3, that condensates can create both alkaline and acidic gradients with a magnitude exceeding one pH unit. Investigations of multicomponent protein/nucleic acid systems further show that heterotypic interactions can modulate condensate pH gradients. We further investigate using a bioinformatics approach the diversity of electrochemical properties of complex condensates by studying a large set of human condensate networks, showing that these span a wide range of mixture pIs and pH-response behaviours. In summary, our results reveal that protein condensation may present a fundamental physico-chemical mechanism for the effective segregation and optimisation of functional processes through changes in the emergent electrochemical microenvironment.Competing Interest StatementT.P.J.K. is the C.T.O. of Transition Bio Inc., G.I. and N.L.Z. are employees of Novo Nordisk and S.A and A.A.H. are members of the scientific advisory board of Dewpoint Therapeutics Inc. The work reported here was not influenced by these affiliations.