Computational Biology of Viruses: From Molecules to Epidemics

A special issue of Viruses (ISSN 1999-4915).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 49979

Special Issue Editor

Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
Interests: computational modelling; data analysis and evolutionary theory of viral infections
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational approaches have been used to study viruses at all levels of organization: from the molecular processes that occur within infected cells, through the dynamics of populations of virions and cells inside infected hosts, up to the level of epidemics and transmission between hosts. This Special Issue invites submissions that involve computational methods (mathematical or simulation modeling, or data analysis) at any (or, for multiscale models, several) of these levels to gain new insights into the fundamental processes, etiology, spread, and evolution of viral infections.

Dr. Viktor Müller
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • mathematical modeling
  • simulation modeling
  • molecular processes
  • within-host dynamics
  • epidemiological models
  • multiscale models

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Published Papers (11 papers)

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Research

18 pages, 1205 KiB  
Article
Robust Phylodynamic Analysis of Genetic Sequencing Data from Structured Populations
Viruses 2022, 14(8), 1648; https://doi.org/10.3390/v14081648 - 27 Jul 2022
Cited by 5 | Viewed by 1332
Abstract
The multi-type birth–death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree [...] Read more.
The multi-type birth–death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world—one with 500 samples and the other with only 175—for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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12 pages, 1544 KiB  
Article
Structural Comparison of Diverse HIV-1 Subtypes using Molecular Modelling and Docking Analyses of Integrase Inhibitors
Viruses 2020, 12(9), 936; https://doi.org/10.3390/v12090936 - 26 Aug 2020
Cited by 8 | Viewed by 2774
Abstract
The process of viral integration into the host genome is an essential step of the HIV-1 life cycle. The viral integrase (IN) enzyme catalyzes integration. IN is an ideal therapeutic enzyme targeted by several drugs; raltegravir (RAL), elvitegravir (EVG), dolutegravir (DTG), and bictegravir [...] Read more.
The process of viral integration into the host genome is an essential step of the HIV-1 life cycle. The viral integrase (IN) enzyme catalyzes integration. IN is an ideal therapeutic enzyme targeted by several drugs; raltegravir (RAL), elvitegravir (EVG), dolutegravir (DTG), and bictegravir (BIC) having been approved by the USA Food and Drug Administration (FDA). Due to high HIV-1 diversity, it is not well understood how specific naturally occurring polymorphisms (NOPs) in IN may affect the structure/function and binding affinity of integrase strand transfer inhibitors (INSTIs). We applied computational methods of molecular modelling and docking to analyze the effect of NOPs on the full-length IN structure and INSTI binding. We identified 13 NOPs within the Cameroonian-derived CRF02_AG IN sequences and further identified 17 NOPs within HIV-1C South African sequences. The NOPs in the IN structures did not show any differences in INSTI binding affinity. However, linear regression analysis revealed a positive correlation between the Ki and EC50 values for DTG and BIC as strong inhibitors of HIV-1 IN subtypes. All INSTIs are clinically effective against diverse HIV-1 strains from INSTI treatment-naïve populations. This study supports the use of second-generation INSTIs such as DTG and BIC as part of first-line combination antiretroviral therapy (cART) regimens, due to a stronger genetic barrier to the emergence of drug resistance. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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38 pages, 2583 KiB  
Article
Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses
Viruses 2020, 12(7), 758; https://doi.org/10.3390/v12070758 - 14 Jul 2020
Cited by 3 | Viewed by 4507
Abstract
Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, [...] Read more.
Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the quantification of intra-host variation through haplotype reconstruction. We validate and compare the consensus sequence assembly methods of HAPHPIPE to those of two alternative software packages, HyDRA and Geneious, using simulated HIV and empirical HIV, HCV, and SARS-CoV-2 datasets. Our validation methods included read mapping, genetic distance, and genetic diversity metrics. In simulated NGS data, HAPHPIPE generated pol consensus sequences significantly closer to the true consensus sequence than those produced by HyDRA and Geneious and performed comparably to Geneious for HIV gp120 sequences. Furthermore, using empirical data from multiple viruses, we demonstrate that HAPHPIPE can analyze larger sequence datasets due to its greater computational speed. Therefore, we contend that HAPHPIPE provides a more user-friendly platform for users with and without bioinformatics experience to implement current best practices for viral NGS assembly than other currently available options. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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12 pages, 2223 KiB  
Article
Bioinformatics Pipeline for Human Papillomavirus Short Read Genomic Sequences Classification Using Support Vector Machine
Viruses 2020, 12(7), 710; https://doi.org/10.3390/v12070710 - 30 Jun 2020
Cited by 4 | Viewed by 2347
Abstract
We recently developed a test based on the Agilent SureSelect target enrichment system capturing genomic fragments from 191 human papillomaviruses (HPV) types for Illumina sequencing. This enriched whole genome sequencing (eWGS) assay provides an approach to identify all HPV types in a sample. [...] Read more.
We recently developed a test based on the Agilent SureSelect target enrichment system capturing genomic fragments from 191 human papillomaviruses (HPV) types for Illumina sequencing. This enriched whole genome sequencing (eWGS) assay provides an approach to identify all HPV types in a sample. Here we present a machine learning algorithm that calls HPV types based on the eWGS output. The algorithm based on the support vector machine (SVM) technique was trained on eWGS data from 122 control samples with known HPV types. The new algorithm demonstrated good performance in HPV type detection for designed samples with 25 or greater HPV plasmid copies per sample. We compared the results of HPV typing made by the new algorithm for 261 residual epidemiologic samples with the results of the typing delivered by the standard HPV Linear Array (LA). The agreement between methods (97.4%) was substantial (kappa = 0.783). However, the new algorithm identified additionally 428 instances of HPV types not detectable by the LA assay by design. Overall, we have demonstrated that the bioinformatics pipeline is an accurate tool for calling HPV types by analyzing data generated by eWGS processing of DNA fragments extracted from control and epidemiological samples. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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30 pages, 1217 KiB  
Article
Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios
Viruses 2020, 12(7), 708; https://doi.org/10.3390/v12070708 - 30 Jun 2020
Cited by 49 | Viewed by 7939
Abstract
COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. [...] Read more.
COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. In this report, we present the first epidemiological and statistical analysis of the early phase of the COVID-19 outbreak in Hungary. Then, we establish an age-structured compartmental model to explore alternative post-lockdown scenarios. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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15 pages, 1858 KiB  
Article
Spatial–Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak
Viruses 2020, 12(6), 588; https://doi.org/10.3390/v12060588 - 27 May 2020
Cited by 27 | Viewed by 4629
Abstract
The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable [...] Read more.
The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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15 pages, 28169 KiB  
Article
Administration of Defective Virus Inhibits Dengue Transmission into Mosquitoes
Viruses 2020, 12(5), 558; https://doi.org/10.3390/v12050558 - 18 May 2020
Cited by 2 | Viewed by 2525
Abstract
The host-vector shuttle and the bottleneck in dengue transmission is a significant aspect with regard to the study of dengue outbreaks. As mosquitoes require 100–1000 times more virus to become infected than human, the transmission of dengue virus from human to mosquito is [...] Read more.
The host-vector shuttle and the bottleneck in dengue transmission is a significant aspect with regard to the study of dengue outbreaks. As mosquitoes require 100–1000 times more virus to become infected than human, the transmission of dengue virus from human to mosquito is a vulnerability that can be targeted to improve disease control. In order to capture the heterogeneity in the infectiousness of an infected patient population towards the mosquito population, we calibrate a population of host-to-vector virus transmission models based on an experimentally quantified infected fraction of a mosquito population. Once the population of models is well-calibrated, we deploy a population of controls that helps to inhibit the human-to-mosquito transmission of the dengue virus indirectly by reducing the viral load in the patient body fluid. We use an optimal bang-bang control on the administration of the defective virus (transmissible interfering particles (TIPs)) to symptomatic patients in the course of their febrile period and observe the dynamics in successful reduction of dengue spread into mosquitoes. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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17 pages, 1224 KiB  
Article
Modelling Degradation and Replication Kinetics of the Zika Virus In Vitro Infection
Viruses 2020, 12(5), 547; https://doi.org/10.3390/v12050547 - 15 May 2020
Cited by 6 | Viewed by 3308
Abstract
Mathematical models of in vitro viral kinetics help us understand and quantify the main determinants underlying the virus–host cell interactions. We aimed to provide a numerical characterization of the Zika virus (ZIKV) in vitro infection kinetics, an arthropod-borne emerging virus that has gained [...] Read more.
Mathematical models of in vitro viral kinetics help us understand and quantify the main determinants underlying the virus–host cell interactions. We aimed to provide a numerical characterization of the Zika virus (ZIKV) in vitro infection kinetics, an arthropod-borne emerging virus that has gained public recognition due to its association with microcephaly in newborns. The mathematical model of in vitro viral infection typically assumes that degradation of extracellular infectious virus proceeds in an exponential manner, that is, each viral particle has the same probability of losing infectivity at any given time. We incubated ZIKV stock in the cell culture media and sampled with high frequency for quantification over the course of 96 h. The data showed a delay in the virus degradation in the first 24 h followed by a decline, which could not be captured by the model with exponentially distributed decay time of infectious virus. Thus, we proposed a model, in which inactivation of infectious ZIKV is gamma distributed and fit the model to the temporal measurements of infectious virus remaining in the media. The model was able to reproduce the data well and yielded the decay time of infectious ZIKV to be 40 h. We studied the in vitro ZIKV infection kinetics by conducting cell infection at two distinct multiplicity of infection and measuring viral loads over time. We fit the mathematical model of in vitro viral infection with gamma distributed degradation time of infectious virus to the viral growth data and identified the timespans and rates involved within the ZIKV-host cell interplay. Our mathematical analysis combined with the data provides a well-described example of non-exponential viral decay dynamics and presents numerical characterization of in vitro infection with ZIKV. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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14 pages, 3833 KiB  
Article
In Silico Discovery of Candidate Drugs against Covid-19
Viruses 2020, 12(4), 404; https://doi.org/10.3390/v12040404 - 06 Apr 2020
Cited by 96 | Viewed by 12143
Abstract
Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated [...] Read more.
Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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16 pages, 2728 KiB  
Article
Evolution of BACON Domain Tandem Repeats in crAssphage and Novel Gut Bacteriophage Lineages
Viruses 2019, 11(12), 1085; https://doi.org/10.3390/v11121085 - 21 Nov 2019
Cited by 17 | Viewed by 4245
Abstract
The human gut contains an expanse of largely unstudied bacteriophages. Among the most common are crAss-like phages, which were predicted to infect Bacteriodetes hosts. CrAssphage, the first crAss-like phage to be discovered, contains a protein encoding a Bacteroides-associated carbohydrate-binding often N-terminal (BACON) [...] Read more.
The human gut contains an expanse of largely unstudied bacteriophages. Among the most common are crAss-like phages, which were predicted to infect Bacteriodetes hosts. CrAssphage, the first crAss-like phage to be discovered, contains a protein encoding a Bacteroides-associated carbohydrate-binding often N-terminal (BACON) domain tandem repeat. Because protein domain tandem repeats are often hotspots of evolution, BACON domains may provide insight into the evolution of crAss-like phages. Here, we studied the biodiversity and evolution of BACON domains in bacteriophages by analysing over 2 million viral contigs. We found a high biodiversity of BACON in seven gut phage lineages, including five known crAss-like phage lineages and two novel gut phage lineages that are distantly related to crAss-like phages. In three BACON-containing phage lineages, we found that BACON domain tandem repeats were associated with phage tail proteins, suggestive of a possible role of these repeats in host binding. In contrast, individual BACON domains that did not occur in tandem were not found in the proximity of tail proteins. In two lineages, tail-associated BACON domain tandem repeats evolved largely through horizontal transfer of separate domains. In the third lineage that includes the prototypical crAssphage, the tandem repeats arose from several sequential domain duplications, resulting in a characteristic tandem array that is distinct from bacterial BACON domains. We conclude that phage tail-associated BACON domain tandem repeats have evolved in at least two independent cases in gut bacteriophages, including in the widespread gut phage crAssphage. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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10 pages, 715 KiB  
Article
MiDRMpol: A High-Throughput Multiplexed Amplicon Sequencing Workflow to Quantify HIV-1 Drug Resistance Mutations against Protease, Reverse Transcriptase, and Integrase Inhibitors
Viruses 2019, 11(9), 806; https://doi.org/10.3390/v11090806 - 30 Aug 2019
Cited by 3 | Viewed by 3349
Abstract
The detection of drug resistance mutations (DRMs) in minor viral populations is of potential clinical importance. However, sophisticated computational infrastructure and competence for analysis of high-throughput sequencing (HTS) data lack at most diagnostic laboratories. Thus, we have proposed a new pipeline, MiDRMpol [...] Read more.
The detection of drug resistance mutations (DRMs) in minor viral populations is of potential clinical importance. However, sophisticated computational infrastructure and competence for analysis of high-throughput sequencing (HTS) data lack at most diagnostic laboratories. Thus, we have proposed a new pipeline, MiDRMpol, to quantify DRM from the HIV-1 pol region. The gag-vpu region of 87 plasma samples from HIV-infected individuals from three cohorts was amplified and sequenced by Illumina HiSeq2500. The sequence reads were adapter-trimmed, followed by analysis using in-house scripts. Samples from Swedish and Ethiopian cohorts were also sequenced by Sanger sequencing. The pipeline was validated against the online tool PASeq (Polymorphism Analysis by Sequencing). Based on an error rate of <1%, a value of >1% was set as reliable to consider a minor variant. Both pipelines detected the mutations in the dominant viral populations, while discrepancies were observed in minor viral populations. In five HIV-1 subtype C samples, minor mutations were detected at the <5% level by MiDRMpol but not by PASeq. MiDRMpol is a computationally as well as labor efficient bioinformatics pipeline for the detection of DRM from HTS data. It identifies minor viral populations (<20%) of DRMs. Our method can be incorporated into large-scale surveillance of HIV-1 DRM. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
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