X-ray photon correlation spectroscopy of hydrated lysozyme at elevated pressures

TL;DR

This study uses X-ray photon correlation spectroscopy (XPCS) in ultra-small-angle geometry to probe hydrated lysozyme dynamics under pressures up to $0.4$ GPa using a diamond anvil cell. An integrated analysis of structure (Porod exponent) and dynamics (g2, relaxation rate Γ, and Kohlrausch exponent α) reveals a non-monotonic response: dynamics slow down up to $0.2$ GPa and subsequently re-accelerate near $0.4$ GPa, with a corresponding crossover in structural indicators. The results suggest pressure-induced rearrangements in local protein packing that affect nanoscale stress-relaxation, linking hydration-mediated dynamics to compression-driven transitions. These insights advance understanding of protein-water interactions under pressure and have implications for high-pressure food processing and pharmaceutical applications.

Abstract

Pressure provides a powerful parameter to control the protein conformation state, which at sufficiently high values can lead to unfolding. Here, we investigate the effects of increasing pressure up to $0.4$ GPa on hydrated lysozyme proteins, by measuring the nanoscale stress relaxation induced and probed by X-rays. Structural and dynamical information at elevated pressures was obtained using X-ray photon correlation spectroscopy (XPCS) in combination with a diamond anvil cell (DAC). The dynamical analysis revealed a slowing down of the system up to $0.2$ GPa, followed by a re-acceleration at $0.4$ GPa. A similar non-monotonic behavior was observed both in the Porod and Kohlrausch-Williams-Watts (KWW) exponents, consistently indicating a crossover between $0.2$ and $0.4$ GPa. These findings suggest the presence of pressure-induced structural changes that impact protein collective stress-relaxation as the system transitions from a jammed state to an elastically driven regime. These results may be relevant for a deeper understanding of protein stability under compression as well as for practical high-pressure technologies, including food processing and pharmaceutical applications.

Figures (3)

• Figure 1: (a) Scattering intensities $I(q)$ as a function of momentum transfer $q$, averaged over several datasets for each pressure and normalized to the intensity at $q \approx 0.04$ nm$^{-1}$. The inset shows the relative intensities of all elevated pressures normalized to the intensity at ambient pressure. (b) The Porod exponents $n$, averaged over all individual exponents extracted using Eq. \ref{['eq:porod']} for each pressure. The error bars were calculated using the standard error. The dashed curve denote a parabolic fit to highlight the trend.
• Figure 2: (a) The normalized intensity autocorrelation function $g_2(q,t)$ at momentum transfer $q=0.07$ nm$^{-1}$, with the baseline $B$ subtracted and normalized to the contrast $\beta$. All pressures are shown and have been averaged over several measurements at the same pressure. (b) Relaxation rates $\Gamma$, extracted from the $g_2$ functions, as a function of momentum transfer $q$, averaged over all measurements for each pressure and $q$-value. The dashed lines denote a linear fit made to the relaxation rates. (c) The Kohlrausch-Williams-Watts (KWW) exponents $\alpha$, extracted from the $g_2$ functions, as a function of pressure, averaged over all measurements for each pressure. Error bars are given by the standard error of the extracted values. The dashed curve denote a parabolic fit to highlight the trend.
• Figure 3: The two-time correlation functions with the baseline subtracted and normalized to the diagonal at momentum transfer $q=0.07$ nm$^{-1}$ for all pressures, as indicated by each panel header.