What determines sub-diffusive behavior in crowded protein solutions?

Vijay Phanindra Srikanth Kompella, Maria Carmen Romano, Ian Stansfield, Ricardo L. Mancera* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

The aqueous environment inside cells is densely packed. A typical cell has a macromolecular concentration in the range 90?450 g/L, with 5%?40% of its volume being occupied by macromolecules, resulting in what is known as macromolecular crowding. The space available for the free diffusion of metabolites and other macromolecules is thus greatly reduced, leading to so-called excluded volume effects. The slow diffusion of macromolecules under crowded conditions has been explained using transient complex formation. However, sub-diffusion noted in earlier works is not well characterized, particularly the role played by transient complex formation and excluded volume effects. We have used Brownian dynamics simulations to characterize the diffusion of chymotrypsin inhibitor 2 in protein solutions of bovine serum albumin and lysozyme at concentrations ranging from 50 to 300 g/L. The predicted changes in diffusion coefficient as a function of crowder concentration are consistent with NMR experiments. The sub-diffusive behavior observed in the sub-microsecond timescale can be explained in terms of a so-called cage effect, arising from rattling motion in a local molecular cage as a consequence of excluded volume effects. By selectively manipulating the nature of interactions between protein molecules, we determined that excluded volume effects induce sub-diffusive dynamics at sub-microsecond timescales. These findings may help to explain the diffusion-mediated effects of protein crowding on cellular processes.
Original languageEnglish
Pages (from-to)134-146
Number of pages13
JournalBiophysical Journal
Volume123
Issue number2
Early online date16 Jan 2024
DOIs
Publication statusPublished - 16 Jan 2024

Bibliographical note

This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk), access to which was provided by the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim (https://www.hecbiosim.ac.uk/), supported by EPSRC (grant no. EP/R029407/1). Analysis and visualization of the simulation data were conducted at the Pawsey Supercomputing Centre, therefore this work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia, as well as resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. V.K. gratefully acknowledges the receipt of a scholarship under the Aberdeen-Curtin Alliance collaborative PhD program.

Data Availability Statement

The data associated with this work, which include the input files, MSD data, analysis scripts, and other relevant files, are all publicly available on Zenodo (https://doi.org/10.5281/zenodo.8412942).

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