Noncovalent antibody catenation on a target surface greatly increases the antigen-binding avidity.

Immunoglobulin G (IgG) antibodies are widely used for diagnosis and therapy. Given the unique dimeric structure of IgG, we hypothesized that, by genetically fusing a homodimeric protein (catenator) to the C-terminus of IgG, reversible catenation of antibody molecules could be induced on a surface where target antigen molecules are abundant, and that it could be an effective way to greatly enhance the antigen-binding avidity. A thermodynamic simulation showed that quite low homodimerization affinity of a catenator, e.g. dissociation constant of 100 µM, can enhance nanomolar antigen-binding avidity to a picomolar level, and that the fold enhancement sharply depends on the density of the antigen. In a proof-of-concept experiment where antigen molecules are immobilized on a biosensor tip, the C-terminal fusion of a pair of weakly homodimerizing proteins to three different antibodies enhanced the antigen-binding avidity by at least 110 or 304 folds from the intrinsic binding avidity. Compared with the mother antibody, Obinutuzumab(Y101L) which targets CD20, the same antibody with fused catenators exhibited significantly enhanced binding to SU-DHL5 cells. Together, the homodimerization-induced antibody catenation would be a new powerful approach to improve antibody applications, including the detection of scarce biomarkers and targeted anticancer therapies.

Are Non-Structural Proteins From SARS-CoV-2 the Target of Hydroxychloroquine? An in Silico Study

COVID-19 is caused by SARS-CoV-2 which has structural and non-structural proteins (NSP) essential for infection and viral replication. There is a possible binding of SARS-CoV-2 to the beta-1 chain of hemoglobin in red blood cells and thus, decreasing the oxygen transport capacity. Since hydroxychloroquine (HCQ) can accumulate in red cells, there is a chance of interaction of this drug with the virus. To analyze possible interactions between SARS-CoV-2 NSP and hemoglobin with the HCQ using molecular docking and implications for the infected host. This research consisted of a study using bioinformatics tools. The files of the protein structures and HCQ were prepared using the AutoDock Tools software. These files were used to perform molecular docking simulations by AutoDock Vina. The binding affinity report of the generated conformers was analyzed using PyMol software, as well as the chemical bonds formed. The results showed that HCQ is capable of interacting with both SARS-CoV-2 NSP and human hemoglobin. The HCQ/NSP3 conformer, HCQ/NSP5, HCQ/NSP7-NSP8-NSP12, HCQ/NSP9, HCQ/NSP10-NSP16 showed binding affinity. In addition, the interaction between HCQ and hemoglobin resulted in polar bonds. Interaction between SARS-CoV-2 NSP and HCQ indicates that this drug possibly acts by preventing the continuity of infection.Copyright © 2023 Tehran University of Medical Sciences.

Computational approaches in COVID-19 vaccine development

The deadly outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that began in Wuhan city of China, late in 2019, has caused thousands of causalities globally, where the infected subjects show severe respiratory illness, fever, and pneumonia-like symptoms. The efforts to design a safe, cost-effective, and most importantly efficient coronavirus disease 2019 (COVID-19) vaccine have been fruitful so far, and approximately 10 vaccines have been approved by the World Health Organization and many more in trials. However, this virus possesses the exceptional ability to rapidly mutate and spread at an exponential level. Research and development activities around the world, directed at vaccine development, were accelerated after the SARS-CoV-2 gene sequence was made publicly available. The economic and humanitarian pressure of the ongoing COVID-19 pandemic is necessitating evaluation of alternative vaccine production platforms and the use of innovative paradigms to speed up the development. Hence, more determination is required to develop vaccines that have higher efficacy and specificity. Some of the regimes being followed are discussed in this chapter along with the current developments. © 2023 Elsevier Inc. All rights reserved.

Systems biology in COVID-19

Coronavirus disease 2019 (COVID-19), the disease cause by the novel severe acute respiratory syndrome coronavirus 2 represents a global, unresolved challenge for researchers and clinicians alike. In the shadow of overwhelmed healthcare systems, the pressure to produce knowledge, standard operating procedures, efficacious treatments, and prophylactic agents has been unlike any other occasion in recent history. Systems biology, an assortment of methods that aim to model biological systems and their properties has risen to meet this multifaceted challenge. In this chapter, we review approaches and breakthroughs of systems biology research in COVID-19, along with the nascent clade of phenomics, a deep-phenotyping systems concept that has enabled the real-time integration of big data and analytical methods in clinical decision making. © 2023 Elsevier Inc. All rights reserved.

US National Institutes of Health Prioritization of SARS-CoV-2 Variants.

Since late 2020, SARS-CoV-2 variants have regularly emerged with competitive and phenotypic differences from previously circulating strains, sometimes with the potential to escape from immunity produced by prior exposure and infection. The Early Detection group is one of the constituent groups of the US National Institutes of Health National Institute of Allergy and Infectious Diseases SARS-CoV-2 Assessment of Viral Evolution program. The group uses bioinformatic methods to monitor the emergence, spread, and potential phenotypic properties of emerging and circulating strains to identify the most relevant variants for experimental groups within the program to phenotypically characterize. Since April 2021, the group has prioritized variants monthly. Prioritization successes include rapidly identifying most major variants of SARS-CoV-2 and providing experimental groups within the National Institutes of Health program easy access to regularly updated information on the recent evolution and epidemiology of SARS-CoV-2 that can be used to guide phenotypic investigations.

Molecular interaction modeling of carbon nanotubes and fullerene toward prioritized targets of SARS-CoV-2 by computer-aided screening and docking studies

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.

Ginsenoside Rg3, a promising agent for NSCLC patients in the pandemic: A large-scale data mining and systemic biological analysis.

Introduction: Non-small cell lung cancer (NSCLC) patients are particularly vulnerable to the Coronavirus Disease-2019 (COVID-19). Currently, no anti-NSCLC/COVID-19 treatment options are available. As ginsenoside Rg3 is beneficial to NSCLC patients and has been identified as an entry inhibitor of the virus, this study aims to explore underlying pharmacological mechanisms of ginsenoside Rg3 for the treatment of NSCLC patients with COVID-19. Methods: Based on a large-scale data mining and systemic biological analysis, this study investigated target genes, biological processes, pharmacological mechanisms, and underlying immune implications of ginsenoside Rg3 for NSCLC patients with COVID-19. Results: An important gene set containing 26 target genes was built. Target genes with significant prognostic value were identified, including baculoviral IAP repeat containing 5 (BIRC5), carbonic anhydrase 9 (CA9), endothelin receptor type B (EDNRB), glucagon receptor (GCGR), interleukin 2 (IL2), peptidyl arginine deiminase 4 (PADI4), and solute carrier organic anion transporter family member 1B1 (SLCO1B1). The expression of target genes was significantly correlated with the infiltration level of macrophages, eosinophils, natural killer cells, and T lymphocytes. Ginsenoside Rg3 may benefit NSCLC patients with COVID-19 by regulating signaling pathways primarily involved in anti-inflammation, immunomodulation, cell cycle, cell fate, carcinogenesis, and hemodynamics. Conclusions: This study provided a comprehensive strategy for drug discovery in NSCLC and COVID-19 based on systemic biology approaches. Ginsenoside Rg3 may be a prospective drug for NSCLC patients with COVID-19. Future studies are needed to determine the value of ginsenoside Rg3 for NSCLC patients with COVID-19.

Structural evolution of Delta lineage of SARS-CoV-2.

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

Formal Concept Analysis Applications in Bioinformatics

The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing. [ FROM AUTHOR]

CriSNPr, a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems.

CRISPR-based diagnostics (CRISPRDx) have improved clinical decision-making, especially during the COVID-19 pandemic, by detecting nucleic acids and identifying variants. This has been accelerated by the discovery of new and engineered CRISPR effectors, which have expanded the portfolio of diagnostic applications to include a broad range of pathogenic and non-pathogenic conditions. However, each diagnostic CRISPR pipeline necessitates customized detection schemes based on the fundamental principles of the Cas protein used, its guide RNA (gRNA) design parameters, and the assay readout. This is especially relevant for variant detection, a low-cost alternative to sequencing-based approaches for which no in silico pipeline for the ready-to-use design of CRISPRDx currently exists. In this manuscript, we fill this lacuna using a unified web server, CriSNPr (CRISPR-based SNP recognition), which provides the user with the opportunity to de novo design gRNAs based on six CRISPRDx proteins of choice (Fn/enFnCas9, LwCas13a, LbCas12a, AaCas12b, and Cas14a) and query for ready-to-use oligonucleotide sequences for validation on relevant samples. Furthermore, we provide a database of curated pre-designed gRNAs as well as target/off-target for all human and SARS-CoV-2 variants reported thus far. CriSNPr has been validated on multiple Cas proteins, demonstrating its broad and immediate applicability across multiple detection platforms. CriSNPr can be found at http://crisnpr.igib.res.in/.

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis.

It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.

Computational study of some potential inhibitors of COVID-19: A DFT analysis

Background: There is an urgent demand of drug or therapy to control the COVID-19. Until July 22, 2021 the worldwide total number of cases reported is more than 192 million and the total number of deaths reported is more than 4.12 million. Several countries have given emergency permission for use of repurposed drugs for the treatment of COVID-19 patients. This report presents a computational analysis on repurposing drugs-tenofovir, bepotastine, epirubicin, epoprostenol, tirazavirin, aprepitant and valrubicin, which can be potential inhibitors of the COVID-19.Method: Density functional theory (DFT) technique is applied for computation of these repurposed drug. For geometry optimization, functional B3LYP/6-311G (d, p) is selected within DFT framework.Results: DFT based descriptors-highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index, nucleophilicity index and dipole moment of these species are computed. IR and Raman activities are also analysed and studied. The result shows that the HOMO-LUMO gap of these species varies from 1.061 eV to 5.327 eV. Compound aprepitant with a HOMO-LUMO gap of 1.419 eV shows the maximum intensity of IR (786.176 km mol-1) and Raman spectra (15036.702 a.u.).Conclusion: Some potential inhibitors of COVID-19 are studied by using DFT technique. This study shows that epirubicin is the most reactive compound whereas tenofovir is found to be the most stable. Further analysis and clinical trials of these compounds will provide more insight.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV).

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

The effect of notification window length on the epidemiological impact of COVID-19 contact tracing mobile applications.

Background: The reduction in SARS-CoV-2 transmission facilitated by mobile contact tracing applications (apps) depends both on the proportion of relevant contacts notified and on the probability that those contacts quarantine after notification. The proportion of relevant contacts notified depends upon the number of days preceding an infector's positive test that their contacts are notified, which we refer to as an app's notification window. Methods: We use an epidemiological model of SARS-CoV-2 transmission that captures the profile of infection to consider the trade-off between notification window length and active app use. We focus on 5-day and 2-day windows, the notification windows of the NHS COVID-19 app in England and Wales before and after 2nd August 2021, respectively. Results: Our analyses show that at the same level of active app use, 5-day windows result in larger reductions in transmission than 2-day windows. However, short notification windows can be more effective at reducing transmission if they are associated with higher levels of active app use and adherence to isolation upon notification. Conclusions: Our results demonstrate the importance of understanding adherence to interventions when setting notification windows for COVID-19 contact tracing apps.

After submitting a positive SARS-CoV-2 test result, mobile contact-tracing apps identify 'recent' high-risk encounters with other app users, who are then notified of potential exposure. An app's success at limiting further transmission depends on the proportion of infected contacts notified. This depends on what counts as 'recent', e.g. notifying contacts from 5 days prior to the positive test can capture more infections than notifying contacts from 2 days prior. We call this number of days an app's notification window. However, an app's effectiveness also depends on whether or not exposed contacts use the app and adhere to isolation if notified. If shorter windows are associated with higher levels of active app use, they can be more effective at reducing transmission than longer windows, demonstrating the importance of considering the potential impact on active app use when setting an app's notification window length.

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes.

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19.

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.