Genetic algorithm application for the prediction of potential SARS-CoV-2 new variant of concern

The COVID-19 pandemic motivated an intense debate over high transmissibility and unavailability of effective vaccine to cover all existent variants, and also has raised critical questions, such as concerns about new mutations and genetic recombination that could lead to novel variants of concerns. The density of mutation observed in the different residue indices of spike protein sequence, may correlate to the speed of virus distribution. Therefore, predicting an accurate determination of mutation rates is essential to comprehend this virus evolution and assess the risk of emergent infectious disease. The current study predicts the mutations that may be cause of new variants of concerns using a genetic algorithm approach. In this regard, we mutated randomly the wild-type sequence of SARS-CoV-2 spike protein to generate first 100 different sequences (initial population) that were modelled individually and used to evaluate their discrete optimized protein energy score. After applying cross-over and breeding 200 new generations, one of the sequences with the lowest discrete optimized protein energy score was identified and chosen for a further analysis to realize whether this sequence is potential for being the next variant of concern. © 2022 IEEE.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Lung Organoids-The Ultimate Tool to Dissect Pulmonary Diseases?

Organoids are complex multicellular three-dimensional (3D) in vitro models that are designed to allow accurate studies of the molecular processes and pathologies of human organs. Organoids can be derived from a variety of cell types, such as human primary progenitor cells, pluripotent stem cells, or tumor-derived cells and can be co-cultured with immune or microbial cells to further mimic the tissue niche. Here, we focus on the development of 3D lung organoids and their use as disease models and drug screening tools. We introduce the various experimental approaches used to model complex human diseases and analyze their advantages and disadvantages. We also discuss validation of the organoids and their physiological relevance to the study of lung diseases. Furthermore, we summarize the current use of lung organoids as models of host-pathogen interactions and human lung diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or SARS-CoV-2 infection. Moreover, we discuss the use of lung organoids derived from tumor cells as lung cancer models and their application in personalized cancer medicine research. Finally, we outline the future of research in the field of human induced pluripotent stem cell-derived organoids.

Design Analysis and User Research of Elevator Disinfection Equipment Based on Hall 3D Structure

With the spread of COVID-19,elevators as a confined space and a frequently used tool in human life, have a very urgent need for disinfection. Currently, most elevator disinfection products on the market focus on local disinfection. However, when elevators carry a large number of people and the distance between people is too close, the probability of virus transmission is greatly increased. Then simple local disinfection will not meet the high disinfection requirements. At this point, the expectations generated by the overall disinfection product increase. In this paper, a hard systems approach-Hall 3D structure is used to create a 3D model for the design of elevator disinfection equipment based on the propagation environment of COVID-19. The design process can be carried out in a smooth manner with continuous progress and optimization. This paper presents the whole process of investigation and experimentation for the design of elevator disinfection equipment in a temporal dimension, complemented by a logical dimension and a knowledge dimension to help designers get timely feedback, identify problems in the design process, and conduct actual user experience. The design of the elevator disinfection device was finalized through experimental research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Recent advances in RNA structurome.

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

Essentials of the COVID-19 coronavirus

This chapter presents the essential characteristics of the coronavirus disease 2019 (COVID-19) coronavirus in terms of the physical, chemical, and biological attributes of the mutant strains causing the current global pandemic, with symptoms ranging from fever;dry cough;tiredness;loss of taste, smell, and speech;sore throat;and chest pain to difficulty in breathing and constituting a threat to the existence of humanity. Preliminary in silico studies of retrieved sequences for coronavirus isolates from some endemic countries, presented in this chapter, extensively revealed the true characteristics of the coronavirus isolates, ranging from molecular weight, total number of atoms, aliphatic index, instability index, extinction coefficient, theoretic isoelectric point, grand hydropathicity, total number of negatively and positively charged amino acids residues, secondary protein structure characteristics, variations in the tertiary protein 3D structures, and the guanine-cytosine content in the RNA sequence of the isolates. Preliminary in silico determination of genetic and thermal stability potentials of the isolates has also been revealed using the instability index, aliphatic index, guanine-cytosine content, hydropathicity, and half-life of the isolates in human reticulocytes in vitro. The scary characteristics of the coronaviruses were revealed in their ability to mutate at a faster rate producing many mutant copies of the virus that are not exact, thus conferring on it the ability to escape the host immune system. This probably is responsible for the resurgence of the viruses with varied characteristics and antigens that differ from the previous strains, thus giving room for the risk of a pandemic. This calls for a more concerted effort in studying the essentials and mutation rates of the viruses to be able to predict the future mutation rate and possible attributes with a view to finding a suitable therapy and drug design for the pandemic and for the biosecurity of humans against the virus in the future. © 2022 Elsevier Inc.

The Convergence of HPC, AI and Big Data in Rapid-Response to the COVID-19 Pandemic

The “Force for Good” pledge of intellectual property to fight COVID-19 brought into action HPE products, resources and expertise to the problem of drug/vaccine discovery. Several scientists and technologists collaborated to accelerate efforts towards a cure. This paper documents the spirit of such a collaboration, the stellar outcomes and the technological lessons learned from the true convergence of high-performance computing (HPC), artificial intelligence (AI) and data science to fight a pandemic. The paper presents technologies that assisted in an end-to-end edge-to-supercomputer pipeline - creating 3D structures of the virus from CryoEM microscopes, filtering through large cheminformatics databases of drug molecules, using artificial intelligence and molecular docking simulations to identify drug candidates that may bind with the 3D structures of the virus, validating the binding activity using in-silico high-fidelity multi-body physics simulations, combing through millions of literature-based facts and assay data to connect-the-dots of evidence to explain or dispute the in-silico predictions. These contributions accelerated scientific discovery by: (i) identifying novel drug molecules that could reduce COVID-19 virality in the human body, (ii) screening drug molecule databases to design wet lab experiments faster and better, (iii) hypothesizing the cross-immunity of Tetanus vaccines based on comparisons of COVID-19 and publicly available protein sequences, and (iv) prioritizing drug compounds that could be repurposed for COVID-19 treatment. We present case studies around each of the aforementioned outcomes and posit an accelerated future of drug discovery in an augmented and converged workflow of data science, high-performance computing and artificial intelligence. © 2022, Springer Nature Switzerland AG.

Structural analysis of SARS-Cov-2 nonstructural protein 1 polymorphisms found in the Brazilian Amazon.

The coronavirus disease COVID-19 has been the cause of millions of deaths worldwide. Among the SARS-CoV-2 proteins, the non-structural protein 1 (NSP1) has great importance during the virus infection process and is present in both alpha and beta-CoVs. Therefore, monitoring of NSP1 polymorphisms is crucial in order to understand their role during infection and virus-induced pathogenicity. Herein, we analyzed how mutations detected in the circulating SARS-CoV-2 in the population of the city of Manaus, Amazonas state, Brazil could modify the tertiary structure of the NSP1 protein. Three mutations were detected in the SARS-CoV-2 NSP1 gene: deletion of the amino acids KSF from positions 141 to 143 (delKSF), SARS-CoV-2, lineage B.1.195; and two substitutions, R29H and R43C, SARS-CoV-2 lineage B.1.1.28 and B.1.1.33, respectively. The delKSF was found in 47 samples, whereas R29H and R43C were found in two samples, one for each mutation. The NSP1 structures carrying the mutations R43C and R29H on the N-terminal portion (e.g. residues 10 to 127) showed minor backbone divergence compared to the Wuhan model. However, the NSP1 C-terminal region (residues 145 to 180) was severely affected in the delKSF and R29H mutants. The intermediate variable region (residues 144 to 148) leads to changes in the C-terminal region, particularly in the delKSF structure. New investigations must be carried out to analyze how these changes affect NSP1 activity during the infection. Our results reinforce the need for continuous genomic surveillance of SARS-CoV-2 to better understand virus evolution and assess the potential impact of the viral mutations on the approved vaccines and future therapies.

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping.

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.

A combination of cross-neutralizing antibodies synergizes to prevent SARS-CoV-2 and SARS-CoV pseudovirus infection.

Coronaviruses have caused several human epidemics and pandemics including the ongoing coronavirus disease 2019 (COVID-19). Prophylactic vaccines and therapeutic antibodies have already shown striking effectiveness against COVID-19. Nevertheless, concerns remain about antigenic drift in SARS-CoV-2 as well as threats from other sarbecoviruses. Cross-neutralizing antibodies to SARS-related viruses provide opportunities to address such concerns. Here, we report on crystal structures of a cross-neutralizing antibody, CV38-142, in complex with the receptor-binding domains from SARS-CoV-2 and SARS-CoV. Recognition of the N343 glycosylation site and water-mediated interactions facilitate cross-reactivity of CV38-142 to SARS-related viruses, allowing the antibody to accommodate antigenic variation in these viruses. CV38-142 synergizes with other cross-neutralizing antibodies, notably COVA1-16, to enhance neutralization of SARS-CoV and SARS-CoV-2, including circulating variants of concern B.1.1.7 and B.1.351. Overall, this study provides valuable information for vaccine and therapeutic design to address current and future antigenic drift in SARS-CoV-2 and to protect against zoonotic SARS-related coronaviruses.

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids.

BACKGROUND: In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people's health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein-protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. RESULTS: In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. CONCLUSIONS: In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

An overview of viruses discovered over the last decades and drug development for the current pandemic.

Since the discovery of the yellow fever virus in 1901, thus far, two hundred nineteen viral species are recognized as human pathogens. Each year, the number of viruses causing infections in humans increases, triggering epidemics and pandemics, such as the current COVID-19 pandemic. Pointing to bats as the natural host, in 2019, a genome highly identical to a bat coronavirus (COVID-19) spread all over the world, and the World Health Organization (WHO) officially confirmed it as a pandemic. The virus mainly spreads through the respiratory tract, uses angiotensin-converting enzyme 2 (ACE2) as a receptor, and is characterized by symptoms of fever, cough, and fatigue. Antivirals and vaccines have provided improvements in some cases, but the discovery of a new and diverse variety of viruses with outbreaks has posed a challenge in timely treatments for medical scientists. Currently, few specific antiviral strategies are being used, and many of the effective antiviral drugs and reported active molecules are under vital exploration. In this review, with the details of viral diseases, we summarize the current attempts in drug development, epidemiology, and the latest treatments and scientific advancements to combat the COVID-19 epidemic. Moreover, we discuss ways to reduce epidemics and pandemics in the near future.