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Viruses
Special Issues
Computational Modeling in RNA Viruses
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Computational Modeling in RNA Viruses

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "General Virology".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2100

Special Issue Editor

Dr. Rui Yin

E-Mail Website
Guest Editor
Department of Health Outcome and Biomedical Informatics, University of Florida, Gainesville, FL, USA
Interests: machine learning; RNA viruses; bioinformatics; genomic analysis

Special Issue Information

Dear Colleagues,

RNA viruses pose unique challenges due to their high mutation rates and rapid evolution, making them responsible for numerous infectious diseases in humans, animals, and plants. This Special Issue revolves around the utilization of computational techniques and models, e.g., machine learning and statistics, to gain insights into the behavior, dynamics, and characteristics of RNA viruses. By employing computational modeling approaches, we aim to enhance our understanding of RNA viruses' complex mechanisms, such as viral replication, transmission, and immune system interactions. These models can simulate viral spread within populations, predict the impact of interventions such as vaccination or antiviral treatments, and assist in the design of effective control strategies. The areas to be covered in this RNA virus research topic may include, but are not limited to, the following:

  • Viral replication modeling;
  • Viral evolution and phylogenetics;
  • Host–virus interactions;
  • Drug discovery and design;
  • Vaccine design and optimization;
  • Transmission dynamics and epidemiology;
  • Drug resistance and antiviral therapy;
  • Structural biology and protein modeling;
  • Antigenicity, pathogenicity, and virulence estimation;
  • Development of tools to interrogate and annotate viruses.

Dr. Rui Yin
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. Viruses 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 2600 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

  • computational modelling
  • machine learning
  • viral evolution
  • genomics
  • infectious diseases
  • public health
  • host-pathogen interaction
  • protein modelling
  • drug discovery
  • vaccine design

Published Papers (2 papers)

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Research

20 pages, 11110 KiB  
Article
Assessing pH-Dependent Conformational Changes in the Fusion Peptide Proximal Region of the SARS-CoV-2 Spike Glycoprotein
by Darya Stepanenko, Yuzhang Wang and Carlos Simmerling
Viruses 2024, 16(7), 1066; https://doi.org/10.3390/v16071066 - 2 Jul 2024
Viewed by 412
Abstract
One of the entry mechanisms of the SARS-CoV-2 coronavirus into host cells involves endosomal acidification. It has been proposed that under acidic conditions, the fusion peptide proximal region (FPPR) of the SARS-CoV-2 spike glycoprotein acts as a pH-dependent switch, modulating immune response accessibility [...] Read more.
One of the entry mechanisms of the SARS-CoV-2 coronavirus into host cells involves endosomal acidification. It has been proposed that under acidic conditions, the fusion peptide proximal region (FPPR) of the SARS-CoV-2 spike glycoprotein acts as a pH-dependent switch, modulating immune response accessibility by influencing the positioning of the receptor binding domain (RBD). This would provide indirect coupling of RBD opening to the environmental pH. Here, we explored this possible pH-dependent conformational equilibrium of the FPPR within the SARS-CoV-2 spike glycoprotein. We analyzed hundreds of experimentally determined spike structures from the Protein Data Bank and carried out pH-replica exchange molecular dynamics to explore the extent to which the FPPR conformation depends on pH and the positioning of the RBD. A meta-analysis of experimental structures identified alternate conformations of the FPPR among structures in which this flexible regions was resolved. However, the results did not support a correlation between the FPPR conformation and either RBD position or the reported pH of the cryo-EM experiment. We calculated pKa values for titratable side chains in the FPPR region using PDB structures, but these pKa values showed large differences between alternate PDB structures that otherwise adopt the same FPPR conformation type. This hampers the comparison of pKa values in different FPPR conformations to rationalize a pH-dependent conformational change. We supplemented these PDB-based analyses with all-atom simulations and used constant-pH replica exchange molecular dynamics to estimate pKa values in the context of flexibility and explicit water. The resulting titration curves show good reproducibility between simulations, but they also suggest that the titration curves of the different FPPR conformations are the same within the error bars. In summary, we were unable to find evidence supporting the previously published hypothesis of an FPPR pH-dependent equilibrium: neither from existing experimental data nor from constant-pH MD simulations. The study underscores the complexity of the spike system and opens avenues for further exploration into the interplay between pH and SARS-CoV-2 viral entry mechanisms. Full article
(This article belongs to the Special Issue Computational Modeling in RNA Viruses)
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15 pages, 3159 KiB  
Article
Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning
by Yiheng Liu, Zitong He, Liyiyang Jia, Yiwei Xue, Yuxuan Du, Huiwen Tan, Xianzhi Zhang, Yu Ji, Yigang Tong, Haijun Xu and Luo Liu
Viruses 2024, 16(3), 477; https://doi.org/10.3390/v16030477 - 20 Mar 2024
Viewed by 1111
Abstract
Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain [...] Read more.
Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of −110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (−101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the “ML-Guided Design Correctly Predicts Combinatorial Effects Strategy” compared to the “ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy”. To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development. Full article
(This article belongs to the Special Issue Computational Modeling in RNA Viruses)
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