Back

Optically driven control of mechanochemistry and fusion dynamics of biomolecular condensates via thymine dimerization

Sheikhhassani, V.; Wong, F. F. H. K.; Bonn, D.; Schmit, J. D.; Mashaghi, A.

2025-04-03 biophysics
10.1101/2025.03.30.646143 bioRxiv
Show abstract

Phase-separated biomolecular condensates serve as functional elements of biological cells, contribute to protocell formation in prebiotic systems during early life, and represent a distinct class of soft matter with a broad range of potential applications. Understanding and controlling condensate mechanochemistry is critical for their function and material properties. Photochemical processes, such as UV-induced chemical modifications, are ubiquitous in nature and can have both detrimental and constructive impacts on living systems, and are also readily implemented in engineering applications. However, how phase-separated condensate formation influences photochemical processes, and conversely, how photochemical reactions impact condensate dynamics, remains an open question. Combining scanning probe microscopy with optical imaging and control, we developed assays that enable the study of mechanical transitions and fusion dynamics in condensate droplets, revealing that UV-induced thymine dimerization alters condensate nucleation and coalescence. Depending on the frequency and topological arrangement of thymine dimers, particularly the balance between inter- and intrachain crosslinks, UV can induce a transition from liquid-like to solid-like behaviours or lead to aggregate formation. UV treatment also leads to compartmentalization in condensate systems by e.g., promoting the formation of arrested fusion droplets, which are stable against environmental changes. UV illumination can thus be leveraged to program the architecture and material properties of DNA-based biomolecular condensates, with implications for prebiotic chemistry, and bio-inspired engineering.

Published in Nature Communications (predicted rank #5) · training set

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.