Modeling Quasielastic Neutron Scattering from Protein Powders
Modeling Quasielastic Neutron Scattering from Protein Powders lead image
Quasielastic neutron scattering (QENS) is a powerful technique that can probe system-averaged diffusive motions of individual hydrogen atoms in condensed or soft matter, such as proteins, on time scales in the pico- to nanosecond range. In this paper, a new method to analyze QENS data is introduced and applied to experimental data for the human acetylcholinesterase (hAChE) enzyme.
A prior analysis by this group of the same data used a minimalistic multi-scale model. The results revealed subtle but systematic changes due to noncovalent binding of the inhibitor Huperzine A to hAChE. The authors attributed these changes to slight increases in the amplitude of hydrogen atom movements along with a slight decrease in the diffusivity of their motions.
To remove the influence of resolution broadening by the instrument, the earlier analysis was carried out in the time domain, which necessitated a numerical Fourier transform of the QENS spectrum. In the current paper, the investigators have found a way to carry out the analysis directly in the frequency domain, avoiding the numerical Fourier transform, which is known to produce aliasing errors.
The new approach requires convolution of the ideal model dynamic structure factor with an instrumental resolution function. The authors perform the convolution in Laplace space, using a Padé approximation for the Laplace-transformed resolution function. They carried out a frequency-domain analysis with the same model as for the earlier time domain analysis, confirming the earlier results. This suggests the technique could be a promising new method for analysis of QENS spectra for other biomolecular systems.
Source: “Frequency domain modeling of quasielastic neutron scattering from hydrated protein powders: Application to free and inhibited human Acetylcholinesterase,” by Melek Saouessi, Judith Peters, and Gerald R. Kneller, Journal of Chemical Physics (2019) The article can be accessed at https://doi.org/10.1063/1.5121703 .
