Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
COVID
Special Issues
SARS-CoV-2 Bioinformatics
share announcement

SARS-CoV-2 Bioinformatics

A special issue of COVID (ISSN 2673-8112).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 11266

Special Issue Editor

Dr. Fernando Barroso

E-Mail Website
Guest Editor
1. Departamento de Ciências Biomoleculares, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
2. Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
Interests: molecular biophysics; SARS-CoV-2 variants; structural bioinformatics; computer simulations; biomolecular engineering

Special Issue Information

Dear Colleagues,

SARS-CoV-2 emerged as a global threat and challenge. Throughout the COVID-19 pandemic and since its early days, computational works have been conducted from a better understanding of the virus to develop diagnostic tools, treatments, and vaccines. This was possible due to an already long history of success stories of computer simulations contributing to the understanding of complex systems in biology, pharmacology, and immunology. From the kinetics of the polymerization reaction of viruses and epidemiologic models to the design of antiviral molecules, a broad spectrum of studied phenomena illustrated the contributions from in silico works. A vast number of different theoretical approaches have been developed and applied and are nowadays boosted by the resurgence of artificial intelligence strategies. Rhinovirus, HIV, tobacco mosaic virus, Dengue, and Zika, among other systems, contribute to form this solid background for computational virology and immunoinformatics. Stepping on such a basis, the computational studies of SARS-CoV-2 together with the abundant source of experimental data promoted this research field to another level. Now is the time to highlight these in silico contributions and their importance in our fight against COVID-19. This is the main purpose of this Special Issue.

Moreover, often, theoretical papers are scattered in journals on different subjects due to the intrinsic multidisciplinary aspect of this research field. A Special Issue such as the present one offers the opportunity to portray the main contributions achieved at present in a single forum. Published in a common Special Issue, they can be a practical reference source for the topic and catalyze the cross-polymerization of the different computational approaches. High-quality scientific papers applying computational simulation methods to characterize any biological aspect of SARS-CoV-2, its infectivity, transmission, pathogenesis, diagnosis, treatments, and prevention are welcome. In particular, we would like to receive contributions that offer breakthrough contributions to the field. Works presenting new computational methods and tools are also welcome. Pure computational papers across different scales can be accepted providing they discuss the relevant experimental and/or clinical literature.

Dr. Fernando Barroso
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. COVID is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer simulation
  • molecular dynamics
  • Monte Carlo
  • artificial inteligence
  • docking
  • bioinformatics
  • Immunoinformatics
  • host-pathogen interactions
  • antibody
  • vaccine development

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 2371 KiB  
Article
Genomic Surveillance of SARS-CoV-2 Sequence Variants at Universities in Southwest Idaho
by Jennifer R. Chase, Laura Bond, Daniel J. Vail, Milan Sengthep, Adriana Rodriguez, Joe Christianson, Stephanie F. Hudon and Julia Thom Oxford
COVID 2024, 4(1), 23-37; https://doi.org/10.3390/covid4010003 - 25 Dec 2023
Viewed by 1449
Abstract
Although the impact of the SARS-CoV-2 pandemic on major metropolitan areas is broadly reported and readily available, regions with lower populations and more remote areas in the United States are understudied. The objective of this study is to determine the progression of SARS-CoV-2 [...] Read more.
Although the impact of the SARS-CoV-2 pandemic on major metropolitan areas is broadly reported and readily available, regions with lower populations and more remote areas in the United States are understudied. The objective of this study is to determine the progression of SARS-CoV-2 sequence variants in a frontier and remote intermountain west state among university-associated communities. This study was conducted at two intermountain west universities from 2020 to 2022. Positive SARS-CoV-2 samples were confirmed by quantitative real-time reverse transcription-polymerase chain reaction and variants were identified by the next-generation sequencing of viral genomes. Positive results were obtained for 5355 samples, representing a positivity rate of 3.5% overall. The median age was 22 years. Viral genomic sequence data were analyzed for 1717 samples and phylogeny was presented. Associations between viral variants, age, sex, and reported symptoms among 1522 samples indicated a significant association between age and the Delta variant (B 1.167.2), consistent with the findings for other regions. An outbreak event of AY122 was detected August–October 2021. A 2-month delay was observed with respect to the timing of the first documented viral infection within this region compared to major metropolitan regions of the US. Full article
(This article belongs to the Special Issue SARS-CoV-2 Bioinformatics)
►▼ Show Figures

Figure 1

9 pages, 1673 KiB  
Communication
Genetic Analysis and Epitope Prediction of SARS-CoV-2 Genome in Bahia, Brazil: An In Silico Analysis of First and Second Wave Genomics Diversity
by Gabriela Andrade, Guilherme Matias, Lara Chrisóstomo, João da Costa-Neto, Juan Sampaio, Arthur Silva and Isaac Cansanção
COVID 2023, 3(5), 655-663; https://doi.org/10.3390/covid3050047 - 23 Apr 2023
Viewed by 1912
Abstract
COVID-19 is an infectious disease caused by SARS-CoV-2. This virus presents high levels of mutation and transmissibility, which contributed to the emergence of the pandemic. Our study aimed to analyze, in silico, the genomic diversity of SARS-CoV-2 strains in Bahia State by comparing [...] Read more.
COVID-19 is an infectious disease caused by SARS-CoV-2. This virus presents high levels of mutation and transmissibility, which contributed to the emergence of the pandemic. Our study aimed to analyze, in silico, the genomic diversity of SARS-CoV-2 strains in Bahia State by comparing patterns in variability of strains circulating in Brazil with the first isolated strain NC_045512 (reference sequence). Genomes were collected using GISAID, and subsequently aligned and compared using structural and functional genomic annotation. A total of 744 genomes were selected, and 20,773 mutations were found, most of which were of the SNP type. Most of the samples presented low mutational impact, and of the samples, the P.1 (360) lineage possessed the highest prevalence. The most prevalent epitopes were associated with the ORF1ab protein, and in addition to P.1, twenty-one other lineages were also detected during the study period, notably B.1.1.33 (78). The phylogenetic tree revealed that SARS-CoV-2 variants isolated from Bahia were clustered closely together. It is expected that the data collected will help provide a better epidemiological understanding of the COVID-19 pandemic (especially in Bahia), as well as helping to develop more effective vaccines that allow less immunogenic escape. Full article
(This article belongs to the Special Issue SARS-CoV-2 Bioinformatics)
►▼ Show Figures

Figure 1

20 pages, 5246 KiB  
Article
Comparative Analysis and Classification of SARS-CoV-2 Spike Protein Structures in PDB
by Memoona Aslam, M. Saqib Nawaz, Philippe Fournier-Viger and Wenjin Li
COVID 2023, 3(4), 452-471; https://doi.org/10.3390/covid3040034 - 29 Mar 2023
Cited by 2 | Viewed by 3711
Abstract
The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of [...] Read more.
The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of this virus. Analysis and classification of newly determined S protein structures for SARS-CoV-2 are critical to properly understand their functional, evolutionary and architectural relatedness to already known protein structures. In this paper, first, the comparative analysis of SARS-CoV-2 S protein structures is performed. Through comparative analysis, the S protein structures in the PDB (protein data bank) database are compared and analyzed not only with each other but with the structures of other viruses for various parameters. Second, the S protein structures in PDB are classified into different variants, and the associated published literature is studied to investigate what kind of therapeutics (antibodies, T-cell receptors and small molecules) are used on the structures. This is the first study that classifies the S protein structures of the SARS-CoV-2 in PDB into various variants, and the obtained comparative analysis results could be beneficial to the research community, in general, and to crystallographers and health workers, in particular. Full article
(This article belongs to the Special Issue SARS-CoV-2 Bioinformatics)
►▼ Show Figures

Figure 1

17 pages, 2425 KiB  
Article
In Silico Screening of Prospective MHC Class I and II Restricted T-Cell Based Epitopes of the Spike Protein of SARS-CoV-2 for Designing of a Peptide Vaccine for COVID-19
by Kishore Sarma, Nargis K. Bali, Neelanjana Sarmah and Biswajyoti Borkakoty
COVID 2022, 2(12), 1731-1747; https://doi.org/10.3390/covid2120124 - 30 Nov 2022
Cited by 1 | Viewed by 2778
Abstract
Multiple vaccines were developed and administered to immunize people worldwide against SARS-CoV-2 infection. However, changes in platelet count following the course of vaccination have been reported by many studies, suggesting vaccine-induced thrombocytopenia. In this context, designing an effective targeted subunit vaccine with high [...] Read more.
Multiple vaccines were developed and administered to immunize people worldwide against SARS-CoV-2 infection. However, changes in platelet count following the course of vaccination have been reported by many studies, suggesting vaccine-induced thrombocytopenia. In this context, designing an effective targeted subunit vaccine with high specificity and efficiency for people with low platelet counts has become a challenge for researchers. Using the in silico-based approaches and methods, the present study explored the antigenic epitopes of the spike protein of SARS-CoV-2 involved in initial binding of the virus with the angiotensin converting enzyme-2 receptor (ACE-2) on the respiratory epithelial cells. The top ten major histocompatibility complex-I (MHC-I) and MHC-II restricted epitopes were found to have 95.26% and 99.99% HLA-class-I population coverage, respectively. Among the top ten promiscuous MHC-I restricted epitopes, ’FTISVTTEI’ had the highest global HLA population coverage of 53.24%, with an antigenic score of 0.85 and a docking score of −162.4 Kcal/mol. The epitope ‘KLNDLCFTNV’ had the best antigenic score of 2.69 and an HLA population coverage of 43.4% globally. The study predicted and documented the most suitable epitopes with the widest global HLA coverage for synthesis of an efficient peptide-based vaccine against the deadly COVID-19. Full article
(This article belongs to the Special Issue SARS-CoV-2 Bioinformatics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop