Special Issue "Computation to Fight SARS-CoV-2 (CoVid-19)"

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editors

Dr. Simone Brogi
Website1 Website2 SciProfiles
Guest Editor
Department of Pharmacy, University of Pisa, Via Bonanno 6 56126 Pisa, Italy
Interests: in silico pharmacology; computational toxicology; computer-aided drug design; modeling and simulation
Special Issues and Collections in MDPI journals
Prof. Vincenzo Calderone
Website
Guest Editor
Department of Pharmacy, University of Pisa, Via Bonanno 6 56126 Pisa, Italy
Interests: preclinical pharmacology; cardiovascular pharmacology; drug discovery
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The occurrence of a new, extremely pathogenic betacoronavirus, SARS-CoV-2 (2019-nCoV), is responsible for the CoVid-19 pandemic health emergency. Accordingly, SARS-CoV-2 represents a serious global health warning characterized by high mortality, a high contagion rate, and a lack of clinically approved drugs and vaccines. In order to find safe and effective therapeutic options to treat this infectious disease, computer science could play an extremely relevant role to better understand the virus pathogenic mechanism as well as to propose novel therapeutic strategies. Accordingly, due to progress in computer science, in silico methodologies in medicinal chemistry, pharmacology, biology, genetics, and virology cover relevant tasks in modern research in these fields. Furthermore, due to the current global health warning, such computational techniques could speed up research in order to provide innovative and targeted approaches to fight the coronavirus emergency. In light of this, this Special Issue will highlight progress in terms of drug discovery, virus biology, and epidemiology to provide researchers with the most innovative computer-driven methodologies for fighting SARS-CoV-2.

For this Special Issue of Computation, we invite researchers in the fields of computational drug discovery (including drug repurposing approaches), computational biology/genetics, virology, bioinformatics, and epidemiology to submit original research, short communications, and review articles related to the use of computation to fight SARS-CoV-2.

Dr. Simone Brogi
Prof. Vincenzo Calderone
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 papers will be 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. Computation is an international peer-reviewed open access quarterly 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

  • SARS-CoV-2
  • CoVid-19
  • Quantitative system pharmacology
  • Bioinformatics
  • Computational genetics
  • Computational biology
  • Virology
  • Epidemiology
  • Computer-aided drug design

Published Papers (6 papers)

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Research

Open AccessArticle
Causal Modeling of Twitter Activity during COVID-19
Computation 2020, 8(4), 85; https://doi.org/10.3390/computation8040085 - 29 Sep 2020
Abstract
Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single [...] Read more.
Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
A Computational Study to Identify Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro) from Eucalyptus Active Compounds
Computation 2020, 8(3), 79; https://doi.org/10.3390/computation8030079 - 09 Sep 2020
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be a severe threat to global public health in late 2019. Nevertheless, no approved medicines have been found to inhibit the virus effectively. Anti-malarial and antiviral medicines have been reported to target the [...] Read more.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be a severe threat to global public health in late 2019. Nevertheless, no approved medicines have been found to inhibit the virus effectively. Anti-malarial and antiviral medicines have been reported to target the SARS-CoV-2 virus. This paper chose eight natural eucalyptus compounds to study their binding interactions with the SARS-CoV-2 main protease (Mpro) to assess their potential for becoming herbal drugs for the new SARS-CoV-2 infection virus. In-silico methods such as molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics Poisson Boltzmann Surface Area (MM/PBSA) analysis were used to examine interactions at the atomistic level. The results of molecular docking indicate that Mpro has good binding energy for all compounds studied. Three docked compounds, α-gurjunene, aromadendrene, and allo-aromadendrene, with highest binding energies of −7.34 kcal/mol (−30.75 kJ/mol), −7.23 kcal/mol (−30.25 kJ/mol), and −7.17 kcal/mol (−29.99 kJ/mol) respectively, were simulated with GROningen MAchine for Chemical Simulations (GROMACS) to measure the molecular interactions between Mpro and inhibitors in detail. Our MD simulation results show that α-gurjunene has the strongest binding energy of −20.37 kcal/mol (−85.21 kJ/mol), followed by aromadendrene with −18.99 kcal/mol (−79.45 kJ/mol), and finally allo-aromadendrene with −17.91 kcal/mol (−74.95 kJ/mol). The findings indicate that eucalyptus may be used to inhibit the Mpro enzyme as a drug candidate. This is the first computational analysis that gives an insight into the potential role of structural flexibility during interactions with eucalyptus compounds. It also sheds light on the structural design of new herbal medicinal products against Mpro. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach
Computation 2020, 8(3), 77; https://doi.org/10.3390/computation8030077 - 08 Sep 2020
Abstract
To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. [...] Read more.
To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning
Computation 2020, 8(3), 74; https://doi.org/10.3390/computation8030074 - 24 Aug 2020
Abstract
The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place [...] Read more.
The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?
Computation 2020, 8(2), 59; https://doi.org/10.3390/computation8020059 - 23 Jun 2020
Cited by 2
Abstract
As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination [...] Read more.
As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
Computational View toward the Inhibition of SARS-CoV-2 Spike Glycoprotein and the 3CL Protease
Computation 2020, 8(2), 53; https://doi.org/10.3390/computation8020053 - 31 May 2020
Cited by 3
Abstract
Since the outbreak of the 2019 novel coronavirus disease (COVID-19), the medical research community is vigorously seeking a treatment to control the infection and save the lives of severely infected patients. The main potential candidates for the control of viruses are virally targeted [...] Read more.
Since the outbreak of the 2019 novel coronavirus disease (COVID-19), the medical research community is vigorously seeking a treatment to control the infection and save the lives of severely infected patients. The main potential candidates for the control of viruses are virally targeted agents. In this short letter, we report our calculations on the inhibitors for the SARS-CoV-2 3CL protease and the spike protein for the potential treatment of COVID-19. The results show that the most potent inhibitors of the SARS-CoV-2 3CL protease include saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, etc. Ergotamine, amphotericin b, and vancomycin are most promising to block the interaction of the SARS-CoV-2 S-protein with human ACE-2. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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