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Special Issue "Bioinformatics and Computational Biology of Viruses"

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

Deadline for manuscript submissions: closed (28 February 2016)

Special Issue Editor

Guest Editor
Dr. Viktor Müller

Institute of Biology, Eötvös Loránd University, Pázmány P. s. 1/C, 1117 Budapest, Hungary
Website | E-Mail
Fax: +36 1 381 2188
Interests: virus dynamics; HIV evolution and epidemiology

Special Issue Information

Dear Colleagues,

Computational and systems biology, and bioinformatics play an increasingly important role in virology, helping us understand the structure, functioning and interactions of viral molecules, the within-host dynamics of virus infections, the spread of virus epidemics, and the origin and evolution of viruses, as well as the effect and optimal design of human interventions from drug discovery to epidemics control. This Special Issue invites submissions of modelling and bioinformatics papers from all fields of virology at all levels of organization. The scope of methods covers all in silico approaches such as mathematical and simulation models; molecular dynamics; phylogenetic, genomic and network analyses, and analyses of other complex data sets.

Dr. Viktor Müller
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 1500 CHF (Swiss Francs).

Related Special Issue

Published Papers (9 papers)

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Research

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Open AccessArticle Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A Virtual Screening and Structure-Based Drug Design Study
Viruses 2015, 7(12), 6642-6660; doi:10.3390/v7122963
Received: 1 July 2015 / Revised: 16 November 2015 / Accepted: 30 November 2015 / Published: 15 December 2015
PDF Full-text (1956 KB) | HTML Full-text | XML Full-text
Abstract
Human coronaviruses represent a significant disease burden; however, there is currently no antiviral strategy to combat infection. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) less than 10 years later demonstrates the potential of coronaviruses
[...] Read more.
Human coronaviruses represent a significant disease burden; however, there is currently no antiviral strategy to combat infection. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) less than 10 years later demonstrates the potential of coronaviruses to cross species boundaries and further highlights the importance of identifying novel lead compounds with broad spectrum activity. The coronavirus 3CLpro provides a highly validated drug target and as there is a high degree of sequence homology and conservation in main chain architecture the design of broad spectrum inhibitors is viable. The ZINC drugs-now library was screened in a consensus high-throughput pharmacophore modeling and molecular docking approach by Vina, Glide, GOLD and MM-GBSA. Molecular dynamics further confirmed results obtained from structure-based techniques. A highly defined hit-list of 19 compounds was identified by the structure-based drug design methodologies. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds is bioactive is excellent. Additionally, the compounds segregate into 15 significantly dissimilar (p < 0.05) clusters based on shape and features, which represent valuable scaffolds that can be used as a basis for future anti-coronaviral inhibitor discovery experiments. Importantly though, the enriched subset of 19 compounds identified from the larger library has to be validated experimentally. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
Open AccessArticle Composite Sequence–Structure Stability Models as Screening Tools for Identifying Vulnerable Targets for HIV Drug and Vaccine Development
Viruses 2015, 7(11), 5718-5735; doi:10.3390/v7112901
Received: 31 July 2015 / Revised: 19 October 2015 / Accepted: 23 October 2015 / Published: 4 November 2015
Cited by 1 | PDF Full-text (2385 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Rapid evolution and high sequence diversity enable Human Immunodeficiency Virus (HIV) populations to acquire mutations to escape antiretroviral drugs and host immune responses, and thus are major obstacles for the control of the pandemic. One strategy to overcome this problem is to focus
[...] Read more.
Rapid evolution and high sequence diversity enable Human Immunodeficiency Virus (HIV) populations to acquire mutations to escape antiretroviral drugs and host immune responses, and thus are major obstacles for the control of the pandemic. One strategy to overcome this problem is to focus drugs and vaccines on regions of the viral genome in which mutations are likely to cripple function through destabilization of viral proteins. Studies relying on sequence conservation alone have had only limited success in determining critically important regions. We tested the ability of two structure-based computational models to assign sites in the HIV-1 capsid protein (CA) that would be refractory to mutational change. The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%. Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity. Based on the predicted stability, we identified 45 CA sites prone to destabilizing mutations. More than half of these sites are targets of one or more known CA inhibitors. The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals. Lastly, a joint scoring metric that takes into account both sequence conservation and protein structure stability performed better at identifying deleterious mutations than sequence conservation or structure stability information alone. The computational sequence–structure stability approach proposed here might therefore be useful for identifying immutable sites in a protein for experimental validation as potential targets for drug and vaccine development. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
Open AccessArticle Longitudinal Antigenic Sequences and Sites from Intra-Host Evolution (LASSIE) Identifies Immune-Selected HIV Variants
Viruses 2015, 7(10), 5443-5475; doi:10.3390/v7102881
Received: 31 July 2015 / Revised: 1 October 2015 / Accepted: 5 October 2015 / Published: 21 October 2015
Cited by 2 | PDF Full-text (2881 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations
[...] Read more.
Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations is experimentally and computationally challenging. With current technology, many more viral sequences can readily be obtained than can be tested for binding and neutralization, making down-selection necessary. Typically, this is done manually, by picking variants that represent different time-points and branches on a phylogenetic tree. Such strategies are likely to miss many relevant mutations and combinations of mutations, and to be redundant for other mutations. Longitudinal Antigenic Sequences and Sites from Intrahost Evolution (LASSIE) uses transmitted founder loss to identify virus “hot-spots” under putative immune selection and chooses sequences that represent recurrent mutations in selected sites. LASSIE favors earliest sequences in which mutations arise. With well-characterized longitudinal Env sequences, we confirmed selected sites were concentrated in antibody contacts and selected sequences represented diverse antigenic phenotypes. Practical applications include rapidly identifying immune targets under selective pressure within a subject, selecting minimal sets of reagents for immunological assays that characterize evolving antibody responses, and for immunogens in polyvalent “cocktail” vaccines. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
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Open AccessArticle Sequence and Structure Analysis of Distantly-Related Viruses Reveals Extensive Gene Transfer between Viruses and Hosts and among Viruses
Viruses 2015, 7(10), 5388-5409; doi:10.3390/v7102882
Received: 3 August 2015 / Revised: 8 October 2015 / Accepted: 9 October 2015 / Published: 19 October 2015
Cited by 1 | PDF Full-text (8772 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The origin and evolution of viruses is a subject of ongoing debate. In this study, we provide a full account of the evolutionary relationships between proteins of significant sequence and structural similarity found in viruses that belong to different classes according to the
[...] Read more.
The origin and evolution of viruses is a subject of ongoing debate. In this study, we provide a full account of the evolutionary relationships between proteins of significant sequence and structural similarity found in viruses that belong to different classes according to the Baltimore classification. We show that such proteins can be found in viruses from all Baltimore classes. For protein families that include these proteins, we observe two patterns of the taxonomic spread. In the first pattern, they can be found in a large number of viruses from all implicated Baltimore classes. In the other pattern, the instances of the corresponding protein in species from each Baltimore class are restricted to a few compact clades. Proteins with the first pattern of distribution are products of so-called viral hallmark genes reported previously. Additionally, this pattern is displayed by the envelope glycoproteins from Flaviviridae and Bunyaviridae and helicases of superfamilies 1 and 2 that have homologs in cellular organisms. The second pattern can often be explained by horizontal gene transfer from the host or between viruses, an example being Orthomyxoviridae and Coronaviridae hemagglutinin esterases. Another facet of horizontal gene transfer comprises multiple independent introduction events of genes from cellular organisms into otherwise unrelated viruses. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
Open AccessArticle Genetic Diversity and Selective Pressure in Hepatitis C Virus Genotypes 1–6: Significance for Direct-Acting Antiviral Treatment and Drug Resistance
Viruses 2015, 7(9), 5018-5039; doi:10.3390/v7092857
Received: 1 June 2015 / Revised: 22 August 2015 / Accepted: 1 September 2015 / Published: 16 September 2015
Cited by 8 | PDF Full-text (2042 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and
[...] Read more.
Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and genotyping assays. In this large-scale analysis, genome-wide diversity and selective pressure was mapped, focusing on positions important for treatment, drug resistance, and resistance testing. A dataset of 1415 full-genome sequences, including genotypes 1–6 from the Los Alamos database, was analyzed. In 44% of all full-genome positions, the consensus amino acid was different for at least one genotype. Focusing on positions sharing the same consensus amino acid in all genotypes revealed that only 15% was defined as pan-genotypic highly conserved (≥99% amino acid identity) and an additional 24% as pan-genotypic conserved (≥95%). Despite its large genetic diversity, across all genotypes, codon positions were rarely identified to be positively selected (0.23%–0.46%) and predominantly found to be under negative selective pressure, suggesting mainly neutral evolution. For NS3, NS5A, and NS5B, respectively, 40% (6/15), 33% (3/9), and 14% (2/14) of the resistance-related positions harbored as consensus the amino acid variant related to resistance, potentially impeding treatment. For example, the NS3 variant 80K, conferring resistance to simeprevir used for treatment of HCV1 infected patients, was present in 39.3% of the HCV1a strains and 0.25% of HCV1b strains. Both NS5A variants 28M and 30S, known to be associated with resistance to the pan-genotypic drug daclatasvir, were found in a significant proportion of HCV4 strains (10.7%). NS5B variant 556G, known to confer resistance to non-nucleoside inhibitor dasabuvir, was observed in 8.4% of the HCV1b strains. Given the large HCV genetic diversity, sequencing efforts for resistance testing purposes may need to be genotype-specific or geographically tailored. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
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Open AccessArticle Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics
Viruses 2015, 7(3), 1189-1217; doi:10.3390/v7031189
Received: 19 December 2014 / Revised: 27 February 2015 / Accepted: 3 March 2015 / Published: 13 March 2015
Cited by 3 | PDF Full-text (983 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection.
[...] Read more.
Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results suggest that, in order to appropriately model early HIV/SIV dynamics, additional factors must be considered in the model development. These may include variability in monkey susceptibility to infection, within-host competition between different viruses for target cells at the initial site of virus replication in the mucosa, innate immune response, and possibly the inclusion of several different tissue compartments. The sobering news is that while an increase in model complexity is needed to explain the available experimental data, testing and rejection of more complex models may require more quantitative data than is currently available. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
Open AccessArticle Bioinformatics Tools for Small Genomes, Such as Hepatitis B Virus
Viruses 2015, 7(2), 781-797; doi:10.3390/v7020781
Received: 11 November 2014 / Revised: 18 December 2014 / Accepted: 10 February 2015 / Published: 16 February 2015
Cited by 3 | PDF Full-text (393 KB) | HTML Full-text | XML Full-text
Abstract
DNA sequence analysis is undertaken in many biological research laboratories. The workflow consists of several steps involving the bioinformatic processing of biological data. We have developed a suite of web-based online bioinformatic tools to assist with processing, analysis and curation of DNA sequence
[...] Read more.
DNA sequence analysis is undertaken in many biological research laboratories. The workflow consists of several steps involving the bioinformatic processing of biological data. We have developed a suite of web-based online bioinformatic tools to assist with processing, analysis and curation of DNA sequence data. Most of these tools are genome-agnostic, with two tools specifically designed for hepatitis B virus sequence data. Tools in the suite are able to process sequence data from Sanger sequencing, ultra-deep amplicon resequencing (pyrosequencing) and chromatograph (trace files), as appropriate. The tools are available online at no cost and are aimed at researchers without specialist technical computer knowledge.
The tools can be accessed at http://hvdr.bioinf.wits.ac.za/SmallGenomeTools, and the source code is available online at https://github.com/DrTrevorBell/SmallGenomeTools. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)

Review

Jump to: Research

Open AccessReview Identifying Recent HIV Infections: From Serological Assays to Genomics
Viruses 2015, 7(10), 5508-5524; doi:10.3390/v7102887
Received: 9 July 2015 / Revised: 12 October 2015 / Accepted: 13 October 2015 / Published: 23 October 2015
Cited by 4 | PDF Full-text (772 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we review serological and molecular based methods to identify HIV infection recency. The accurate identification of recent HIV infection continues to be an important research area and has implications for HIV prevention and treatment interventions. Longitudinal cohorts that follow HIV
[...] Read more.
In this paper, we review serological and molecular based methods to identify HIV infection recency. The accurate identification of recent HIV infection continues to be an important research area and has implications for HIV prevention and treatment interventions. Longitudinal cohorts that follow HIV negative individuals over time are the current gold standard approach, but they are logistically challenging, time consuming and an expensive enterprise. Methods that utilize cross-sectional testing and biomarker information have become an affordable alternative to the longitudinal approach. These methods use well-characterized biological makers to differentiate between recent and established HIV infections. However, recent results have identified a number of limitations in serological based assays that are sensitive to the variability in immune responses modulated by HIV subtypes, viral load and antiretroviral therapy. Molecular methods that explore the dynamics between the timing of infection and viral evolution are now emerging as a promising approach. The combination of serological and molecular methods may provide a good solution to identify recent HIV infection in cross-sectional data. As part of this review, we present the advantages and limitations of serological and molecular based methods and their potential complementary role for the identification of HIV infection recency. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
Open AccessReview Modeling Influenza Virus Infection: A Roadmap for Influenza Research
Viruses 2015, 7(10), 5274-5304; doi:10.3390/v7102875
Received: 28 July 2015 / Revised: 28 September 2015 / Accepted: 28 September 2015 / Published: 12 October 2015
Cited by 6 | PDF Full-text (1075 KB) | HTML Full-text | XML Full-text
Abstract
Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite
[...] Read more.
Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
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