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Computational Prediction of Interactions Between SARS-CoV-2 and Human Protein Pairs by PSSM-Based Images

Year 2023, , 166 - 179, 22.03.2023
https://doi.org/10.17798/bitlisfen.1220301

Abstract

Identifying protein-protein interactions is essential to predict the behavior of the virus and to design antiviral drugs against an infection. Like other viruses, SARS-CoV-2 virus must interact with a host cell in order to survive. Such interaction results in an infection in the host organism. Knowing which human protein interacts with the SARS-CoV-2 protein is an essential step in preventing viral infection. In silico approaches provide a reference for in vitro validation to protein-protein interaction studies by finding interacting protein pair candidates. The representation of proteins is one of the key steps for protein interaction network prediction. In this study, we proposed an image representation of proteins based on position-specific scoring matrices (PSSM). PSSMs are matrices that are obtained from multiple sequence alignments. In each of its cells, there is information about the probability of the occurrence of amino acids or nucleotides. PSSM matrices were handled as gray-scale images and called PSSM images. The main motivation of the study is to investigate whether these PSSM images are a suitable protein representation method. To determine adequate image size, conversion to grayscale images was performed at different sizes. SARS-CoV-2-human protein interaction network prediction based on image classification with siamese neural network and Resnet50 was performed on PSSM image datasets of different sizes. The accuracy results obtained with 200x200 size images and siamese neural network as 0.915, and with 400x400 size images and Resnet50 as 0.922 showed that PSSM images can be used for protein representation.

Supporting Institution

Tubitak

Project Number

122E114

References

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Year 2023, , 166 - 179, 22.03.2023
https://doi.org/10.17798/bitlisfen.1220301

Abstract

Project Number

122E114

References

  • [1] P. Koehl, “Protein structure similarities”. Current opinion in structural biology, 11(3), 348-353, 2001. doi: 10.1016/S0959-440X(00)00214-1.
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  • [5] S. Xing, N. Wallmeroth, K. W. Berendzen, and C. Grefen, “Techniques for the analysis of protein-protein interactions in vivo”. Plant Physiology, 171(2), 727-758,2016. doi: 10.1104/pp.16.00470.
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  • [23] B. Khorsand, A. Savadi, J. Zahiri, and M. Naghibzadeh, “Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network”. Mathematical Biosciences and Engineering, 17(4), 3109-3129, 2020. doi: 10.3934/mbe.2020176.
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  • [33] D. Pirolli, B. Righino, and M. C. De Rosa. “Targeting SARS‐CoV‐2 Spike Protein/ACE2 Protein‐Protein Interactions: a Computational Study”. Molecular Informatics, 2021, 40(6), 2060080.
  • [34] H. J. Lee. “An interactome landscape of SARS-CoV-2 virus-human protein-protein interactions by protein sequence-based multi-label classifiers”. BioRxiv, 2021.
  • [35] E. W. Bell, J. H. Schwartz, P. L. Freddolino, and Y. Zhang. “PEPPI: Whole-proteome protein-protein interaction prediction through structure and sequence similarity, functional association, and machine learning”. Journal of Molecular Biology, 2022, 167530.
  • [36] G. Launay, N. Ceres, and J. Martin. “Non-interacting proteins may resemble interacting proteins: prevalence and implications”. Scientific reports, 2017, 7(1), 1-12.
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  • [42] J. C. Jeong, X. Lin, and X. W. Chen. “On position-specific scoring matrix for protein function prediction”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010, 8(2), 308-315.
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  • [44] A. Mohammadi, J. Zahiri, S. Mohammadi, M. Khodarahmi, and S. S. Arab, “PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles”. Biology Methods and Protocols, 7(1), bpac008, 2022. doi: 10.1093/biomethods/bpac008
  • [45] N. Xiao, D. S. Cao, M. F. Zhu, and Q. S. Xu, “protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences”. Bioinformatics, 31(11), 1857-1859, 2015.
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There are 58 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Zeynep Banu Özger 0000-0003-2614-3803

Zeynep Çakabay 0000-0001-7059-2337

Project Number 122E114
Publication Date March 22, 2023
Submission Date December 16, 2022
Acceptance Date February 26, 2023
Published in Issue Year 2023

Cite

IEEE Z. B. Özger and Z. Çakabay, “Computational Prediction of Interactions Between SARS-CoV-2 and Human Protein Pairs by PSSM-Based Images”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 1, pp. 166–179, 2023, doi: 10.17798/bitlisfen.1220301.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

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E-posta: fbe@beu.edu.tr