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Determinants of Protein-Level Parameters Governing MS2 VLP Reassembly

de Castro Assumpcao, D.; Vinokour, E. S.; Mills, M. M.; Liang, S.; Mills, C. E.; Carvalho da Costa, A.; Kennedy, N. W.; Tullman-Ercek, D.

2025-12-02 biophysics
10.64898/2025.12.02.691839 bioRxiv
Show abstract

MS2 virus-like particles (VLPs) are widely used as protein nanocages for cargo encapsulation, yet in vitro disassembly-reassembly protocols remain poorly standardized, and reassembly yields are reported inconsistently. As a result, the same experiments reported in literature produce widely divergent yields, limiting reproducibility and cross-study comparability. Here, we introduce a cargo-specific, quantitative framework for standardized MS2 VLP reassembly yield determination. We evaluate commonly used disassembly and post-disassembly processing methods and identify practical trade-offs between protein recovery, accessibility, and reproducibility. Reassembly yield is quantified using size exclusion chromatography calibrated against purified VLP standards, enabling robust, cargo-specific yield measurement. Using this framework, we apply a full factorial design of experiments to quantify the individual and combined effects of coat protein concentration, ionic strength, buffer pH, and molecular crowding on reassembly yield. The resulting statistical model explains more than 99% of the explainable variance and its linear fit to the experimental data indicates that optimal reassembly conditions extend beyond those tested to date. Protein concentration and ionic strength dominate reassembly yield, whereas pH and osmolyte concentration contribute more modestly within the tested ranges. Finally, we propose practical guidelines for standardized MS2 VLP disassembly, reassembly, and yield reporting, defining a transferable operating envelope for MS2 VLP reconstruction. While demonstrated here using a single nucleic acid cargo (tr-DNA), the framework is readily extensible to alternative cargos and coat protein variants.

Published in New Biotechnology (predicted rank #1) · training set

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