HivNet: Studying in depth the morphology of HIV-1 virion using Deep Learning
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Abstract
The capacity of transmission electron microscopy (TEM) to distinguish ultrastructure morphology at the nanometer scale makes it useful for a wide range of biomedical imaging applications. TEM has long been a vital tool in the virologist's toolbox because of its capacity to directly visualize virus particles. When used in HIV-1 research, TEM is essential for assessing the actions of inhibitors that obstruct the maturation and morphogenesis phases of the virus lifecycle. However, TEM micrograph fabrication and analysis both involve tedious manual effort. We have built an 8-layer convolutional neural network backbone capable of categorizing HIV-1 virions at various phases of maturity and morphogenesis via the devoted application of computer vision frameworks and machine learning techniques. On a wide range of micrographs made up of various experimental samples and magnifications, our results surpassed both typical CNN backbones and deep residual networks, obtaining 91.33 percent testing accuracy and 85.83 percent validation accuracy. We anticipate that this tool will be useful to a variety of studies.
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[2] F.P.O. Nagler, G. Rake. The use of the electron microscope in diagnosis of variola, vaccinia, and varicella. J Bacteriol, 55 (1) (1948), pp. 45-51.
[3] S. Brenner, R. Horne. A negative staining method for high resolution electron microscopy of viruses. Biochim Biophys Acta, 34 (1959), pp. 103-110.
[4] F. Barre-Sinoussi, J. Chermann, F. Rey, M. Nugeyre, S. Chamaret, J. Gruest, C. Dauguet, C. Axler-Blin, F. Vezinet-Brun, C. Rouzioux, W. Rozenbaum, L. Montagnier. Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (aids). Science, 220 (4599) (1983), pp. 868-871.
[5] S. von Stillfried, P. Boor. Detection methods for SARS-CoV-2 in tissue. Der Pathologe (2021), pp. 1432-1963.
[6] W. Sundquist, H.-G. Kräusslich. HIV-1 assembly, budding, and maturation. Cold Spring Harbor Perspectives Medicine, 2 (2012), Article a006924.
[7] M.A. Accola, Å. Öhagen, H.G. Göttlinger. Isolation of human immunodeficiency virus type 1 cores: Retention of vpr in the absence of p6gag. J Virol, 74 (13) (2000), pp. 6198-6202.
[8] J. Fontana, K.A. Jurado, N. Cheng, N.L. Ly, J.R. Fuchs, R.J. Gorelick, A.N. Engelman, A.C. Steven. Distribution and redistribution of HIV-1 nucleocapsid protein in immature, mature, and integrase-inhibited virions: a role for integrase in maturation. J Virol, 89 (19) (2015), pp. 9765-9780.
[9] J Anderson, C Schiffer, S-K Lee, R Swanstrom. Viral Protease Inhibitors, Springer Berlin Heidelberg, Berlin, Heidelberg (2009), pp. 85-110.
[10] A.B. Kleinpeter, E.O. Freed. HIV-1 maturation: Lessons learned from inhibitors. Viruses, 12 (9) (2020), p. 940.
[11] A. Engelman, G. Englund, J.M. Orenstein, M.A. Martin, R. Craigie Multiple effects of mutations in human immunodeficiency virus type 1 integrase on viral replication. Virol, 69 (5) (1995), pp. 2729-2736.
[12] K.A. Jurado, H. Wang, A. Slaughter, L. Feng, J.J. Kessl, Y. Koh, W. Wang, A. Ballandras-Colas, P.A. Patel, J.R. Fuchs, M. Kvaratskhelia, A. Engelman. Allosteric integrase inhibitor potency is determined through the inhibition of HIV-1 particle maturation. Proc Nat Acad Sci, 110 (21) (2013), pp. 8690-8695.
[13] M. Balakrishnan, S.R. Yant, L. Tsai, C. O’Sullivan, R.A. Bam, A. Tsai, et al. Non-catalytic site HIV-1 integrase inhibitors disrupt core maturation and induce a reverse transcription block in target cells. PLoS One, 8 (9) (2013), p. e74163.
[14] J.L. Elliott, J.E. Eschbach, P.C. Koneru, W. Li, M. Puray Chavez, D. Townsend, et al. Integrase-RNA interactions underscore the critical role of integrase in HIV-1 virion morphogenesis. eLife, 9 (2020), p. e54311.
[15] J.J. Kessl, S.B. Kutluay, D. Townsend, S. Rebensburg, A. Slaughter, R.C. Larue, N. Shkriabai, N. Bakouche, J.R. Fuchs, P.D. Bieniasz, M. Kvaratskhelia. HIV-1 integrase binds the viral RNA genome and is essential during virion morphogenesis. Cell, 166 (5) (2016), pp. 1257-1268.e12.
[16] Rey, Juan S., Li, Wen, Bryer, Alexander J., Beatson, Hagan, Lantz, Christian, Engelman, Alan N., & Perilla, Juan R. (2021). Dataset for deep-learning in-situ classification of HIV-1 virion morphology (07-30-2021). [Dataset] Zenodo.
[17] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278– 2324, 1998.
[18] Hossein Gholamalinezhad, Hossein Khosravi, “Pooling Methods in Deep Neural Networks, a Review,”
arXiv:2009.07485 [cs.CV]
[19] V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Icml, 2010.
[20] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[21] J. Bridle, “Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters,” Advances in neural information processing systems, vol. 2, 1989.
[22] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[23] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
[24] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826.
[25] François Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. Computer Vision and Pattern Recognition (cs.CV). arXiv:1610.02357 [cs.CV].
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| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Computer Science and Informatics Artificial Intelligence Deep Learning Convolutional neural networks Image processing HIV virus cells Immature cells Mature cells Eccentric cells Electron microscopy | ||
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How to Cite
1.
Pandey P, Pandey H, Srivastava K. HivNet: Studying in depth the morphology of HIV-1 virion using Deep Learning. Frontiers Biomed Technol. 2023;.
