Analysis of protein-ligand interactions of SARS-CoV-2 against selective drug using deep neural networks

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

doi:10.26599/bdma.2020.9020007

Natarajan Yuvaraj, Rajan Arshath Raja + Show 4 more

Open Access

https://doi.org/10.26599/bdma.2020.9020007

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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.

Highlights

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has increased the mortality across globe
The main contributions of this work involve the following: (1) We develop a deep learning model that involves in finding the potential drugs against the formation of protein-ligand interactions of SARS-CoV-2 virus from the asymptomatic Indian patients based on the limited datasets collected
The results of Deep Neural Network (DNN) are accurate and it is quick in finding the antidote among the selective drugs using virtual drug screening process

Summary

Introduction

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has increased the mortality across globe. The prevention of anti-drugs is essential in reducing the mortality rate at a quicker rate so as to effectively combat the virus globally[1]. With the advent of computationally intensive methods, the protein sequencing is made less expensive and faster. High-cost experiments and technical difficulties have led to poor accuracy in finding the protein-ligand interaction, where the smaller structural details are found[2]. The primary protein structure (amino acid sequence) and its binding residue help in direct determination of tertiary protein structure. The binding residues are found through the protein properties, but it fails to reveal the complex nature of protein structure and binding residues from the primary structures[3]

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