Mechanical Profiling of Biopolymer Condensates through Acoustic Trapping.

Characterizing the mechanical properties of single colloids is a central problem in soft matter physics. It also plays a key role in cell biology through biopolymer condensates, which function as membraneless compartments. Such systems can also malfunction, leading to the onset of a number of diseases, including many neurodegenerative diseases; the functional and pathological condensates are commonly differentiated by their mechanical signature.

Protein Condensate Atlas from predictive models of heteromolecular condensate composition.

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.

Rapid protein stability prediction using deep learning representations.

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue.

Theoretical and Data-Driven Approaches for Biomolecular Condensates.

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process.

Conformational changes in protein kinase A along its activation cycle are rooted in the folding energetics of cyclic-nucleotide binding domains.

Cyclic-nucleotide binding (CNB) domains are structurally and evolutionarily conserved signaling modules that regulate proteins with diverse folds and functions. Despite a wealth of structural information, the mechanisms by which CNB domains couple cyclic-nucleotide binding to conformational changes involved in signal transduction remain unknown. Here we combined single-molecule and computational approaches to investigate the conformation and folding energetics of the two CNB domains of the regulatory subunit of protein kinase A (PKA).

The cofactor-dependent folding mechanism of Drosophila cryptochrome revealed by single-molecule pulling experiments.

The link between cofactor binding and protein activity is well-established. However, how cofactor interactions modulate folding of large proteins remains unknown. We use optical tweezers, clustering and global fitting to dissect the folding mechanism of Drosophila cryptochrome (dCRY), a 542-residue protein that binds FAD, one of the most chemically and structurally complex cofactors in nature.