Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.1
4.7
Journals
Viruses
Special Issues
Computational Modeling in RNA Viruses
share announcement

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 1226

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 (1 paper)

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, 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 781
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)
►▼ Show Figures

Graphical abstract

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