Innovative Microfluidic and Catalytic Techniques Innovation
Abstract
Hydrogen/Deuterium eXchange (HDX) methods for studying protein dynamics would benefit from millisecond-scale incubations to probe intrinsically disordered proteins, highly dynamic regions, and conformation changes. Here, we investigate droplet microfluidics for rapid mixing to trigger D2O labeling, uniform incubations, and rapid droplet merging for acid quenching in advance of mass spectrometry. A surfactant-free merging approach combining expansion elements for synchronized droplet collision proved robust. The high diffusive flux of D2O and protons enables microsecond mixing to trigger and arrest D2O labeling, respectively, affording the possibility of single millisecond incubations. Droplet HDX processors were used to measure the fast uptake characteristics of a model peptide. Forward exchange measurements demonstrate D2O labeling to be the rate-limiting step, in essence defining 10 milliseconds as the minimum practical incubation time for proteins at room temperature, pD 7.4. With the ability to access millisecond time scales, the fast dynamics of calmodulin, a model of calcium-triggered allostery with rapid conformational switching, was investigated. At 10 milliseconds, we could observe significant deuterium uptake within the well-defined EF-hand Ca2+ binding motifs. These findings demonstrate that millisecond HDX enabled by droplet microfluidics allows areas of heightened plasticity to be detected within a stably folded protein.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.