Small-molecule properties define partitioning into biomolecular condensates.

Biomolecular condensates regulate cellular function by compartmentalizing molecules without a surrounding membrane. Condensate function arises from the specific exclusion or enrichment of molecules. Thus, understanding condensate composition is critical to characterizing condensate function. Whereas principles defining macromolecular composition have been described, understanding of small-molecule composition remains limited. Here we quantified the partitioning of ~1,700 biologically relevant small molecules into condensates composed of different macromolecules. Partitioning varied nearly a million-fold across compounds but was correlated among condensates, indicating that disparate condensates are physically similar. For one system, the enriched compounds did not generally bind macromolecules with high affinity under conditions where condensates do not form, suggesting that partitioning is not governed by site-specific interactions. Correspondingly, a machine learning model accurately predicts partitioning using only computed physicochemical features of the compounds, chiefly those related to solubility and hydrophobicity. These results suggest that a hydrophobic environment emerges upon condensate formation, driving the enrichment and exclusion of small molecules.

Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.