Deep-learning in situ classification of HIV-1 virion morphology

Juan S Rey; Wen Li; Alexander J Bryer; Hagan Beatson; Christian Lantz; Alan N Engelman; Juan R Perilla

doi:10.1016/j.csbj.2021.10.001

Deep-learning in situ classification of HIV-1 virion morphology

Comput Struct Biotechnol J. 2021 Oct 5:19:5688-5700. doi: 10.1016/j.csbj.2021.10.001. eCollection 2021.

Authors

Juan S Rey¹, Wen Li^{2

3}, Alexander J Bryer¹, Hagan Beatson¹, Christian Lantz¹, Alan N Engelman^{2

3}, Juan R Perilla^{1

4}

Affiliations

¹ Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States.
² Department of Medicine, Harvard Medical School, Boston, MA, United States.
³ Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States.
⁴ Data Science Institute, University of Delaware, Newark, DE, United States.

Abstract

Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist's toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.

Keywords: Artificial intelligence; Computer vision; Deep learning; Electron microscopy; HIV-1; Quantitative biology; Virology.

Abstract

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