Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

Abstract

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the 25th of November 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

Neonatal outcomes of pregnant women with COVID-19 in a developing country setup

“…WHO (World Health Organization) termed the disease as novel coronavirus disease (COVID-19) and later on 11th March 2020, WHO declared this disease a global pandemic. 1 As of 4th December 2020, the disease has affected over 60 million individuals, out of which over 45 million people recovered and 1.5 million were declared as deceased. According to various studies, it was found that the novel coronavirus infects all age groups including newborns and elderly, but there are a few groups which are more vulnerable to the infection.…”

Potential of Marine Terpenoids against SARS-CoV-2: An In Silico Drug Development Approach

“…At present, several tools and databases of bioinformatics are available for gene analysis to vaccine/drug development. Mostly, systematic computational screening is the most cost−effective and resource-saving approach towards select the ‘lead’ candidate(s) in the current drug development module [ 18 , 23 , 89 , 101 ]. Overall, bioinformatics tools continuously explore hidden genomic information and mainly identified potential natural lead candidates from different sources against SARS-CoV-2 for further experimental study and validation [ 14 , 100 , 102 ].…”

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

“…In modern days, many different in silico approaches can be used to assist the design of a drug candidate or drug, from data (e.g., text and image) mining (e.g., annotated drug databases, antiviral peptide databases, electronic health patient records…), genome analysis, comparative genomics, multiple sequence alignments, visualization tools for epidemiological studies, analysis of macromolecular interaction networks, structural predictions (e.g., comparative modeling, protein folding…), antibody-drug conjugate, analysis of point mutations, protein docking, various types of molecular simulation engines (e.g., for proteins, peptides, small molecules, cell membrane, DNA, RNA, glycans, and interactions among these molecules…), binding pocket predictions, PROTACs (e.g., degradation of viral protein capsids), transcriptomic profile analysis, virtual screening (from small collections of approved drugs as in drug repositioning or repurposing projects to the screening of ultra-large virtual libraries), hit to lead optimization, drug combination, computational polypharmacology and compound profiling, ADMET prediction, multiparameter optimization methods associated with novel data visualization approaches, systems biology, systems pharmacology, with or without the use of machine learning and artificial intelligence (AI) algorithms depending on the type of methods, available data and the stage of the projects [19] , [77] , [80] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] , [149] , [150] , [151] , [152] , [153] , [154] , [155] , [156] , [157] , [158] , [159] , [160] , [161] , [162] , [163] , [164] , [165] , [166] , [167] , [168] , [169] , …”

“…During the Covid-19 pandemic, in wet and dry laboratories alike, numerous projects were initiated and supported by short-term research grants. In the field of computational and data sciences, a tremendous amount of work has been accomplished (e.g., online tools, open databases) in a short period of time [152] . These developments are of high interest as these approaches can help to design chemical probes, drug candidates, therapeutic peptides or proteins and generate new ideas [214] .…”