AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.6 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks

St. Peter’s Institute of Higher Education and Research, Chennai 600054, India
Information Communication Technology Academy, Chennai 600096, India
Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore 641107, India
Department of Computer Science and Engineering, Jagannath Educational Health and Charitable Trust College of Engineering and Technology, Coimbatore 641105, India
B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India
Department of Computer Science, Government Bikram College of Commerce, Patiala 147001, India
Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh 140406, India
Show Author Information

Abstract

In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network (DNN) model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

References

[1]
A. K. Singh, A. Singh, A. Shaikh, R. Singh, and A. Misra, Chloroquine and hydroxychloroquine in the treatment of COVID-19 with or without diabetes: A systematic search and a narrative review with a special reference to India and other developing countries, Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 3, pp. 241-246, 2020.
[2]
J. Y. Yang, A. Roy, and Y. Zhang, Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinformatics, vol. 29, no. 20, pp. 2588-2595, 2013.
[3]
Y. F. Cui, Q. W. Dong, D. C. Hong, and X. Wang, Predicting protein-ligand binding residues with deep convolutional neural networks, BMC Bioinformatics, vol. 20, no. 1, p. 93, 2019.
[4]
S. Wang, S. Q. Sun, Z. Li, R. Y. Zhang, and J. B. Xu, Accurate de novo prediction of protein contact map by ultra-deep learning model, PLoS Comput. Biol., vol. 13, no. 1, p. e1005324, 2017.
[5]
J. Y. Yang, A. Ro, and Y. Zhang, BioLiP: A semi-manually curated database for biologically relevant ligand-protein interactions, Nucl. Acids Res., vol. 41, no. D1, pp. D1096-D1103, 2012.
[6]
D. Mothay and K. V. Ramesh, Binding site analysis of potential protease inhibitors of COVID-19 using AutoDock, VirusDis., vol. 31, no. 2, pp. 194-199, 2020.
[7]
L. Huang, R. Han, T. Ai, P. X. Yu, H. Kang, Q. Tao, and L. M. Xia, Serial quantitative chest CT assessment of COVID-19: Deep-learning approach, Radiol. Cardiothorac. Imaging, vol. 2, no. 2, p. e200075, 2020.
[8]
L. Li, L. X. Qin, Z. G. Xu, Y. B. Yin, X. Wang, B. Kong, J. J Bai, Y. Lu, Z. H. Fang, Q. Song, et al., Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, , 2020.
[9]
I. D. Apostolopoulos and T. A. Mpesiana, Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., vol. 431, pp. 635-640, 2020.
[10]
C. Butt, J. Gill, D. Chun, and B. A. Babu, Deep learning system to screen coronavirus disease 2019 pneumonia, Appl. Intell., .
[11]
C. S. Zheng, X. B. Deng, Q. Fu, Q. Zhou, J. P. Feng, H. Ma, W. Y. Liu, and X. G. Wang, Deep learning-based detection for COVID-19 from chest CT using weak label, , 2020.
[12]
D. S. Li, D. W. Wang, J. P. Dong, N. N. Wang, H. Huang, H. W. Xu, and C. Xia, False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: Role of deep-learning-based CT diagnosis and insights from two cases, Korean J. Radiol., vol. 21, no. 4, pp. 505-508, 2020.
[13]
H. P. Zhang, K. M. Saravanan, Y. Yang, M. T. Hossain, J. X. Li, X. H. Ren, Y. Pan, and Y. J. Wei, Deep learning based drug screening for novel coronavirus 2019-nCov, Interdiscip. Sci. Comput. Life Sci., .
[14]
M. Torrisi, G. Pollastri, and Q. Le, Deep learning methods in protein structure prediction, Comput. Structural Biotechnol. J., vol. 18, pp. 1301-1310, 2020.
[15]
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol. 323, no. 6088, pp. 533-536, 1986.
[16]
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. L. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, et al., Improved protein structure prediction using potentials from deep learning, Nature, vol. 577, no. 7792, pp. 706-710, 2020.
[17]
J. L. Elman, Finding structure in time, Cogn. Sci., vol. 14, no. 2, pp. 179-211, 1990.
[18]
S. Wang, J. Peng, J. Z. Ma, and J. B. Xu, Protein secondary structure prediction using deep convolutional neural fields, Sci. Rep., vol. 6, no. 1, p. 18 962, 2016.
[19]
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[20]
H. J. Hinz, Thermodynamics of protein-ligand interactions: Calorimetric approaches, Annu. Rev. Biophys. Bioeng., vol. 12, no. 1, pp. 285-317, 1983.
[21]
T. Simonson, G. Archontis, and M. Karplus, Free energy simulations come of age: Protein-ligand recognition, Acc. Chem. Res., vol. 35, no. 6, pp. 430-437, 2002.
[22]
R. Perozzo, G. Folkers, and L. Scapozza, Thermodynamics of protein-ligand interactions: History, presence, and future aspects, J. Recept. Signal Transduct. Res., vol. 24, nos. 1&†2, pp. 1-52, 2004.
[23]
M. Hendlich, A. Bergner, J. Günther, and G. Klebe, Relibase: Design and development of a database for comprehensive analysis of protein-ligand interactions, J. Mol. Biol., vol. 326, no. 2, pp. 607-620, 2003.
[24]
L. Y. Lian, I. L. Barsukov, M. J. Sutcliffe, K. H. Sze, and G. C. Roberts, Protein-ligand interactions: Exchange processes and determination of ligand conformation and protein-ligand contacts, Meth. Enzymol., vol. 239, pp. 657-700, 1994.
[25]
G. Mirceva and A. Kulakov, Improvement of protein binding sites prediction by selecting amino acid residues’ features, J. Struct. Biol., vol. 189, no. 1, pp. 9-19, 2015.
[26]
C. H. Yan and Y. F. Wang, A graph kernel method for DNA-binding site prediction, BMC Syst. Biol., vol. 8, no. S4, p. S10, 2014.
[27]
S. C. Izidoro, R. C. de Melo-Minardi, and G. L. Pappa, GASS: Identifying enzyme active sites with genetic algorithms, Bioinformatics, vol. 31, no. 6, pp. 864-870, 2015.
[28]
B. Park, J. Im, N. Tuvshinjargal, W. Lee, and K. Han, Sequence-based prediction of protein-binding sites in DNA: Comparative study of two SVM models, Comp. Methods Programs Biomed., vol. 117, no. 2, pp. 158-167, 2014.
[29]
G. J. Bartlett, C. T. Porter, N. Borkakoti, and J. M. Thornton, Analysis of catalytic residues in enzyme active sites, J. Mol. Biol., vol. 324, no. 1, pp. 105-121, 2002.
[30]
M. J. J. M. Zvelebil and M. J. E. Sternberg, Analysis and prediction of the location of catalytic residues in enzymes, Protein Eng. Des. Sel., vol. 2, no. 2, pp. 127-138, 1988.
[31]
C. Taroni, S. Jones, and J. M. Thornton, Analysis and prediction of carbohydrate binding sites, Protein Eng. Des. Sel., vol. 13, no. 2, pp. 89-98, 2000.
[32]
M. AlQuraishi, ProteinNet: A standardized data set for machine learning of protein structure, BMC Bioinform., vol. 20, no. 1, p. 311, 2019.
Big Data Mining and Analytics
Pages 76-83
Cite this article:
Yuvaraj N, Srihari K, Chandragandhi S, et al. Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks. Big Data Mining and Analytics, 2021, 4(2): 76-83. https://doi.org/10.26599/BDMA.2020.9020007

1041

Views

90

Downloads

33

Crossref

27

Web of Science

40

Scopus

0

CSCD

Altmetrics

Received: 04 June 2020
Accepted: 18 June 2020
Published: 12 January 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return