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

doi:10.26599/BDMA.2020.9020007

Big Data Mining and Analytics

2021, Vol. 4

Issue (2): 76-83 DOI: 10.26599/BDMA.2020.9020007

Special Section on Artificial Intelligence and Big Data Analytics for Coronavirus (COVID-19)

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

Natarajan Yuvaraj(

),Kannan Srihari^*(

),Selvaraj Chandragandhi(

),Rajan Arshath Raja(

),Gaurav Dhiman(

),Amandeep Kaur(

)

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

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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.

Key words： Deep Neural Network (DNN) coronavirus protein-ligand interactions deep learning clinical healthcare system

Received: 04 June 2020 Published: 09 July 2020

Corresponding Authors: Kannan Srihari E-mail: yraj1989@gmail.com;harionto@gmail.com;chandragandhi09@gmail. com;arshathraja.ru@gmail.com;gdhiman0001@gmail.com;sweetchintoo2008@gmail.com

About author: Natarajan Yuvaraj received the BEng and MEng degrees in computer science engineering from Anna University, Tamil Nadu, India in 2010 and 2012, respectively. He is pursuing the PhD degree in computer science engineering at St. Peter’s Institute of Higher Education and Research, Chennai, India. From 2012 to 2015, he was an assistant professor at various educational institutes. Currently, he is working as a deputy manager of research & development department at Information Communication Technology Academy, Tamil Nadu, India. His research interest includes data mining, wireless sensor networks, and mobile computing. He has filed 5 Indian patents, wrote 2 text books, and published 4 book chapters in Elsevier, Springer, CRC Taylor and Francis, and John Wiley and Sons. He has also published 40 research papers in SCI, Scopus, and international and national conferences.|Kannan Srihari received the MEng and PhD degrees from Anna University, Tamil Nadu, India in 2009 and 2013, respectively. He is currently working as an associate professor at Department of Computer Science and Engineering, SNS College of Engineering, India. He published over 60 papers in international journals. His research area includes semantic search engines, microprocessor, FPGA, big data, and cloud computing. He has guided 6 PhD students and currently he is guiding 4 scholars.|Selvaraj Chandragandhi received the BTech and MTech degrees from Anna University, India in 2008 and 2013, respectively. She is working as an assistant professor at Jagannath Educational Health and Charitable Trust College of Engineering and Technology. She has published 5 Scopus papers. Her research area is IoT, bio medical, and block chain.|Rajan Arshath Raja received the bachelor of engineering degree in electronics and communication engineering from Anna University in 2012. He received the master of engineering degree in communication systems from Sree Sastha Institute of Engineering and Technology in 2014. He is currently pursuing the PhD degree in wireless communication at B. S. Abdur Rahman Crescent Institute of Science and Technology. He is currently working as a senior research associate of research & development department at Information Communication Technology Academy, India. His research interest includes the wireless communication, machine learning, and artificial intelligence. He has filed 2 Indian patents, and published 4 book chapters in Elsevier, Springer, CRC Taylor and Francis, and John Wiley and Sons. He also published 10 research papers in SCI, Scopus, and international and national conferences.|Gaurav Dhiman received the master and PhD degrees in computer engineering from Thapar Institute of Engineering & Technology, Patiala, India in 2015 and 2019, respectively. He is currently working as an assistant professor at Department of Computer Science, Government Bikram College of Commerce, Patiala, India. He was selected as the outstanding reviewer from knowledge-based systems (Elsevier). He has published more than 50 peer-reviewed research papers (indexed in SCI-SCIE) and 5 international books. He is also serving as the guest editor of more than five special issues in various peer-reviewed journals. He is also with editorial board members of Current Chinese Computer Science (Bentham Science), Current Dissertations (Bentham Science), and Journal of Fuzzy Logic and Modeling in Engineering (Bentham Science). His research interest includes single- and multi-objectives optimization (bio-inspired, evolutionary, and quantum), soft computing (type-1 and type-2 fuzzy sets), power systems, and change detection using remotely sensed high-resolution satellite data. His research articles can be found in Knowledge-Based Systems (Elsevier), Advances in Engineering Software (Elsevier), Applied Intelligence (Springer), Journal of Computational Science (Elsevier), Engineering with Computers (Springer), Applied Soft Computing (Elsevier), etc.|Amandeep Kaur received the master and PhD degrees in computer engineering from Thapar Institute of Engineering & Technology, Patiala, India in 2012 and 2020, respectively. She is currently working as an assistant professor at Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, India. Her research interest includes single- and multi-objectives optimization (bio-inspired, evolutionary, and quantum), soft computing (type-1 and type-2 fuzzy sets), power systems, and change detection using remotely sensed high-resolution satellite data.


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Cite this article:

Natarajan Yuvaraj,Kannan Srihari,Selvaraj Chandragandhi,Rajan Arshath Raja,Gaurav Dhiman,Amandeep Kaur. 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.

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http://bigdata.tsinghuajournals.com/10.26599/BDMA.2020.9020007 OR http://bigdata.tsinghuajournals.com/Y2021/V4/I2/76

Ligand	Binding energy (kcal $⋅ mol - ? 1$ )	Bond length (?)
Ketoamide	$-$ 5.80	2.8
Lopinavir	$-$ 6.08	2.7
Nelfinavir	$-$ 7.54	3.0
Remdesivir	$-$ 5.51	2.7
Ritonavir	$-$ 5.96	2.7

Table 1 Binding energy and bond length of COVID-19 protease inhibitors.

Fig. 1 Architecture of DNN.

Fig. 2 Feed-forward DNN with 3 hidden layers.

Fig. 3 Feature extraction of protein structure using artificial neural network.

Fig. 4 Classification accuracy of training models of DNN with three pre-trained networks.

Table 2 Performance of ketoamide drug interaction with SARS-CoV-2 virus over Indian datasets.

Table 3 Performance of lopinavir drug interaction with SARS-CoV-2 virus over Indian datasets.

Table 4 Performance of nelfinavir drug interaction with SARS-CoV-2 virus over Indian datasets.

Table 5 Performance of remdesivir drug interaction with SARS-CoV-2 virus over Indian datasets.

Table 6 Performance of ritonavir drug interaction with SARS-CoV-2 virus over Indian datasets.


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