ijms-logo

Journal Browser

Journal Browser

Computational Diagnostics and Therapeutics for COVID-19 and Other Associated Viral Infections

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Microbiology".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 4545

Special Issue Editors

Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India - 110020 Theoretical Photochemistry and Photophysics Group, Faculty of Chemistry, Wroclaw University of Science and Technology, Wrocław PL−50370, Poland
Interests: molecular crystals; temperature and pressure induced phase transitions; monte carlo simulation; molecular dynamics simulations; Orientational and conformational disorder in molecular crystals
Special Issues, Collections and Topics in MDPI journals
CSIR - North East Institute of Science & Technology (CSIR-NEIST), Jorhat, Assam, India
Interests: computational chemistry; developing scripts; web portals and algorithms for drug discovery; non-covalent interactions; bioinformatics; chemoinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

COVID-19, caused by the SARS-CoV-2 virus, has become the most devastating health care challenge of this century and is associated with over 4.5 M deaths as of August 2021. The outbreaks caused by SARS and MERS coronaviruses, swine- and avian-origin influenza, and Ebola and Zika viruses cannot be overlooked. At the same time, we cannot help but admire the progress made by scientists in the field of experimental and computational drugs and diagnostics development during the last few decades. Obviously, when mankind is in grave danger, as in the current crisis, the only way is to generate the appropriate knowledge to tackle the disaster. Since the arrival of computers in our lives and in almost every branch of science, the importance of computational modeling and data science can hardly be overstated. In particular, enormous progress has been made in high-throughput screening and in the development of reliable and fast computational scoring/ranking approaches for lead compounds identification from huge chemical libraries containing compounds on the order of 106 to 1012. Given that multidrug-resistant variants of viruses are emerging over time and certain viruses such as SARS-CoV-2 have the potential to mutate at a faster rate (as of now at least 10 different variants have been reported in the literature), computational approaches for diagnosis and therapeutics are in huge demand when compared to experimental counterparts which are time consuming, very expensive, and not economically sustainable. This Special Issue is devoted to promoting research in the area of the computational development of diagnostics and therapeutics for COVID-19 and other associated viral infections. We recommend the submission of articles where the results from computational screening (molecular docking) or de novo design are validated using either experimental techniques or by employing more accurate and reliable approaches with superior ranking potential. In particular, we encourage authors to submit articles based on computational approaches for identifying lead compounds which employ scoring functions based on electronic structure theory, machine learning, or deep learning. Regarding the design of diagnostic compounds, the coverage is not limited to optical or infrared probes, but studies devoted to diagnostic platforms using biosensors and bioelectronics will also be considered.

Prof. Dr. Natarajan Arul Murugan
Prof. Dr. G. Narahari Sastry
Guest Editors

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • COVID-19
  • SARS-CoV-2
  • machine learning
  • virtual screening
  • molecular docking
  • binding free-energy calculations
  • druggabiliy prediction

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

11 pages, 22481 KiB  
Article
Improved Binding Affinity of Omicron’s Spike Protein for the Human Angiotensin-Converting Enzyme 2 Receptor Is the Key behind Its Increased Virulence
by Rajender Kumar, Natarajan Arul Murugan and Vaibhav Srivastava
Int. J. Mol. Sci. 2022, 23(6), 3409; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23063409 - 21 Mar 2022
Cited by 30 | Viewed by 3607
Abstract
The new variant of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including the spike protein’s receptor-binding domain (RBD). [...] Read more.
The new variant of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including the spike protein’s receptor-binding domain (RBD). We have computationally investigated the interaction between the RBD of both the wild type and Omicron variant of SARS-CoV-2 with the human angiotensin-converting enzyme 2 (hACE2) receptor using molecular dynamics and molecular mechanics-generalized Born surface area (MM-GBSA)-based binding free energy calculations. The mode of the interaction between Omicron’s RBD with the hACE2 receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has an increased affinity for the hACE2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g., S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. A pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to the binding free energies and suggests that this is one of the key interactions stabilizing the formation of the complex. The resulting structural insights into the RBD:hACE2 complex, the binding mode information within it, and residue-wise contributions to the free energy provide insight into the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents. Full article
Show Figures

Figure 1

Back to TopTop