A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

SARS-CoV-2 vaccine-breakthrough infections (VBIs) by Omicron (B.1.1.529) variant and consequences in structural and functional impact (preprint)

We examined the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for hACE2 and found that the wild and mutant spike proteins were using atomistic molecular dynamics simulations. According to binding free energies calculated during mutagenesis, hACE2 bound Omicron spike more strongly than SARS-CoV-2 wild strain. T95I, D614G, and E484K are three substitutions that significantly contribute to the RBD, corresponding to hACE2 binding energies and a doubling of Omicron spike proteins' electrostatic potential. Omicron appears to bind hACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human {beta}-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

Understanding the activating mechanism of the immune system against COVID-19 by Traditional Indian Medicine: Network pharmacology approach.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for "emergency use" or as an "off-label" drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene-gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1'-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.