ABSTRACT
The global outbreak of COVID-19, caused by SARS-CoV-2, which affected millions of people and killed hundreds of thousands, continues to be the most formidable pandemic ever witnessed by mankind. Although several countries are thriving in terms of advancement of potential therapeutic remedy to combat SARS-CoV-2, a comprehensive solution is yet to be achieved. In exploring various approaches for the drug discovery process, computational biology has proven to be extremely helpful in aiding drug development. Additionally, functionalized carbon nanomaterials such as nanotubes and nano fullerene, which are known to be effective against life-threatening pathogens such as influenza virus, human immunodeficiency virus (HIV), etc., are being considered for use against coronaviruses due to their antiviral properties. This chapter thus presents the applications of carbon nanomaterials employing computational approaches in order to predict and study the binding potential of the functionalized nanoparticles against selected targets of SARS-CoV-2. A brief overview of the virus, its genetic, structural, and functional features, carbon nanoparticles and their properties, and different computational approaches applicable for aiding the process of drug development are presented. Furthermore, the binding potential of carbon nanoparticles toward putative SARS-CoV-2 targets is predicted and the molecular interactions between them are analyzed. Overall, this chapter provides profound insight into the binding potential of functionalized carbon nanomaterials toward the prioritized targets of SARS-CoV-2, which gives scope for experimental studies and future investigation. © 2023 Elsevier Ltd. All rights reserved.
ABSTRACT
Computational biology and bioinformatics resources provide a cutting-edge platform for the screening and development of novel therapeutic agents against probable targets of emerging viral diseases. Emerging viral infections such as COVID-19, Ebola, Nipha andMiddle East respiratory syndrome are some of the potential public health threats reported with high mortality and morbidity. The infections caused by these viruses were recently considered as acute onset immune dysrhythmia syndrome. The altered monocytic, cytokines and chemokines balances observed in several emerging viral infections lead to the acute respiratory distress and multiinflammatory syndromes [1]. Recent studies suggested that many countries were unable to manage the drastic and unexpected onset of these viral outbreaks and such a scenario adversely affected the global economy [2]. There are limited vaccines currently available for most of these viral infections and the vaccine development strategies are extremely tedious and complex. In addition, there are no approved drugs available for most of the emerging viral infections. For example, although the use of non toxic concentrations of 2-deoxy-D-glucose (2-DG), a glucose analog that inhibits the activity of phosphoglucoisomerase in the glycolytic pathway of SARS-CoV-2 is suggested to be one of the promising therapeutic agents for COVID-19, the successful application of this drug is yet to be confirmed [3]. Thus, the present editorial briefly outlines the scope of bioinformatics and computational biology toward the discovery of potential therapeutic agents against various viral diseases.
ABSTRACT
SARS-CoV-2 is the pathogen accountable for the recent COVID-19 outbreak that originated from China in December 2019. There are about a million confirmed cases of the infection, and thousands of deaths have been witnessed across 216 countries worldwide as of first week of July 2020. As no approved drugs are currently available for treating COVID-19, it is an alarming situation calling the need to develop alternative therapeutic agents. In order to address the aforementioned need, the underlying mechanism and structural information of SARS-CoV-2 at the molecular level should be understood. The computational biology approaches such as computer-aided virtual screening and molecular modelling provide a significant breakthrough in understanding the structural aspects of various molecular targets of coronavirus and identification novel lead molecules. Natural molecules are one of the probable alternatives as many of these compounds possess ideal drug likeliness and pharmacokinetic features and might probably be used as lead molecules against various targets of SARS-CoV-2. The current chapter provides an overview of different types of coronaviruses, SARS-CoV-2 and its genetic and structural information, the impact of COVID-19 on various sectors such as health and economy, conventional drugs currently used and their shortcomings, various anti-viral compounds present in nature, the importance of natural lead molecules, computational approaches for molecular modelling of the target proteins, major drug targets that are identified, and virtual screening of herbal-based molecules using molecular docking and molecular dynamic simulation studies. This chapter is thus focused on portraying the relevance of utilizing natural lead molecules by virtual screening and pharmacokinetics prediction for the development of effective lead molecules against SARS-CoV-2.