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Analyzing atomic force microscopy images of virus-like particles by expectation-maximization
Rachel A McCormick1, Nicole M Ralbovsky2, William Gilbraith1
1Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, 19716, USA.
NPJ Vaccines
|June 20, 2024
Summary
Analyzing virus-like particles (VLPs) elasticity using atomic force microscopy (AFM) reveals how these vaccine candidates change. A Gaussian mixture model (GMM) identified distinct VLP states, suggesting stepwise morphological changes.
Area of Science:
- Biophysics
- Vaccine Development
- Nanotechnology
Background:
- Virus-like particles (VLPs) are crucial vaccine antigens for viral diseases.
- Assessing VLP elasticity via atomic force microscopy (AFM) can reveal morphological changes.
- Understanding VLP structural transitions is key to optimizing vaccine efficacy.
Purpose of the Study:
- To investigate the nature of VLP morphological changes (continuous vs. stepwise).
- To apply statistical modeling to AFM data of VLP elasticity.
- To identify distinct states of VLP structural transformation.
Main Methods:
- Utilized atomic force microscopy (AFM) to probe VLP elasticity.
- Employed a Gaussian mixture model (GMM) for data analysis.
- Applied the Expectation-Maximization (EM) algorithm to fit the GMM.
Main Results:
- AFM imaging provided data on VLP morphological changes.
- The GMM successfully identified distinct states within VLP populations.
- Analysis suggested that VLP morphological changes may occur in a stepwise manner.
Conclusions:
- The study demonstrates GMM's utility in analyzing AFM data of VLPs.
- Findings indicate that VLPs may transition through discrete structural states.
- This approach aids in understanding VLP behavior for vaccine design.
