Publications by authors named "Yuanxi Yu"

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays.

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Article Synopsis
  • The study presents neutron-scattering measurements of the density of states (DOS) for water and liquid Fomblin across a range of temperatures, revealing a consistent low-energy linear scaling of the DOS that holds at all temperatures.
  • A notable sharp transition at the melting point of water is observed, where the low-frequency DOS shifts to align with Debye's law, while Fomblin displays a smooth change between two behaviors indicative of its glassy dynamics.
  • Both materials show that the slope a(T) of the DOS increases with temperature in an exponential Arrhenius-like manner, a finding supported by molecular dynamics simulations and consistent with the predictions of instantaneous normal mode (INM) theory.
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  • In nanoscience, liquids exhibit solid-like behaviors at very small scales, such as collective shear waves and elasticity, challenging traditional views where such behaviors are rare.
  • Through experiments on liquid water and glycerol confined in graphene oxide membranes, researchers observed a shift from liquid-like to solid-like properties as confinement increases, specifically in low-frequency dynamics.
  • These findings, supported by molecular dynamics simulations, suggest that tighter confinement slows down liquid relaxation processes, highlighting the potential implications for advancing technologies at the nanoscale.
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Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier.

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Water-in-salt electrolytes (WiSEs) have attracted extensive attention as promising alternatives to organic electrolytes. The limited electrochemical stability windows (ESWs) of aqueous electrolytes are significantly widened by WiSEs. However, the actual ESWs are lower than predicted as the interphase with WiSEs is not as stable as the solid electrolyte interphase (SEI) in conventional lithium-ion batteries.

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The vibrational properties of crystalline bulk materials are well described by Debye theory, which successfully predicts the quadratic ω low-frequency scaling of the vibrational density of states. However, the analogous framework for nanoconfined materials with fewer degrees of freedom has been far less well explored. Using inelastic neutron scattering, we characterize the vibrational density of states of amorphous ice confined inside graphene oxide membranes and we observe a crossover from the Debye ω scaling to an anomalous ω behaviour upon reducing the confinement size L.

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