ORCID Profile
0000-0002-8086-2545
Current Organisations
University of Melbourne
,
Hong Kong University of Science and Technology
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Electrical and Electronic Engineering | Signal Processing | Communications engineering | Wireless Communications | Signal processing | Wireless communication systems and technologies (incl. microwave and millimetrewave) |
Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in the Biological Sciences |
Publisher: Cold Spring Harbor Laboratory
Date: 14-12-2021
DOI: 10.1101/2021.12.12.472315
Abstract: Omicron, the most recent SARS-CoV-2 variant of concern (VOC), harbours multiple mutations in the spike protein that were not observed in previous VOCs. Initial studies suggest Omicron to substantially reduce the neutralizing capability of antibodies induced from vaccines and previous infection. However, its effect on T cell responses remains to be determined. Here, we assess the effect of Omicron mutations on known T cell epitopes and report data suggesting T cell responses to remain broadly robust against this new variant.
Publisher: IEEE
Date: 06-2009
Publisher: Oxford University Press (OUP)
Date: 04-12-2019
DOI: 10.1093/BIOINFORMATICS/BTZ890
Abstract: Patterns of mutational correlations, learnt from protein sequences, have been shown to be informative of co-evolutionary sectors that are tightly linked to functional and/or structural properties of proteins. Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA), to reliably predict co-evolutionary sectors of proteins, while controlling for statistical errors caused by limited data. RoCA was demonstrated on multiple viral proteins, with the inferred sectors showing close correspondences with experimentally-known biochemical domains. To facilitate seamless use of RoCA and promote more widespread application to protein data, here we present a standalone cross-platform package ‘RocaSec’ which features an easy-to-use GUI. The package only requires the multiple sequence alignment of a protein for inferring the co-evolutionary sectors. In addition, when information on the protein biochemical domains is provided, RocaSec returns the corresponding statistical association between the inferred sectors and biochemical domains. The RocaSec software is publicly available under the MIT License at hmedaq/RocaSec. Supplementary data are available at Bioinformatics online.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 02-2010
Publisher: IEEE
Date: 08-2020
Publisher: arXiv
Date: 2017
Publisher: arXiv
Date: 2021
Publisher: Public Library of Science (PLoS)
Date: 21-09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: arXiv
Date: 2014
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.186
Publisher: Oxford University Press (OUP)
Date: 28-06-2019
DOI: 10.1093/BIOINFORMATICS/BTZ524
Abstract: Patterns of mutational correlations, learnt from patient-derived sequences of human immunodeficiency virus (HIV) proteins, are informative of biochemically linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design. By using sequence data for HIV Gag, complemented by model-based simulations, we revealed that certain networks of interacting sites that appear important for vaccine design purposes are not accurately reflected by the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. By incorporating information from the sub-dominant PCs, we identified a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this network, we propose several new candidates for a potent T-cell-based HIV vaccine. Accession numbers of all sequences used and the source code scripts for all analysis and figures reported in this work are available online at araz107/HIV-Gag-Immunogens. Supplementary data are available at Bioinformatics online.
Publisher: arXiv
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Cold Spring Harbor Laboratory
Date: 2022
DOI: 10.1101/2021.12.31.21268591
Abstract: New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical epidemiological model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that strongly affect the transmission rate, both within and outside the Spike protein. We also quantify the effects of travel and competition between different lineages on the inferred transmission effects of mutations. Importantly, our model detects lineages with increased transmission as they arise. We infer significant transmission advantages for the Alpha and Delta variants within a week of their appearances in regional data, when their regional frequencies were only around 1%. Our model thus enables the rapid identification of variants and mutations that affect transmission from genomic surveillance data.
Publisher: IEEE
Date: 07-2016
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 2008
Publisher: arXiv
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2010
Publisher: IEEE
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 07-2015
Publisher: IEEE
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2010
Publisher: Elsevier BV
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 31-03-2023
DOI: 10.1038/S41467-023-37468-Y
Abstract: Viral and host factors can shape SARS-CoV-2 evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within in iduals. Here, we analysed deep sequencing data from 2,820 SARS-CoV-2 respiratory s les with different viral lineages to describe the patterns of within-host ersity under different conditions, including vaccine-breakthrough infections. In unvaccinated in iduals, variant of Concern (VOC) Alpha, Delta, and Omicron respiratory s les were found to have higher within-host ersity and were under neutral to purifying selection at the full genome level compared to non-VOC SARS-CoV-2. Breakthrough infections in 2-dose or 3-dose Comirnaty and CoronaVac vaccinated in iduals did not increase levels of non-synonymous mutations and did not change the direction of selection pressure. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 sequence ersification. Our findings suggest that vaccination does not increase exploration of SARS-CoV-2 protein sequence space and may not facilitate emergence of viral variants.
Publisher: Elsevier BV
Date: 11-2021
DOI: 10.1016/J.ORET.2021.01.013
Abstract: To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO). Observational, cross-sectional study. A total of 9392 UWF-SLO images of 1903 eyes from 1022 subjects with diabetes from Hong Kong, the United Kingdom, India, and Argentina. All images were labeled according to the presence or absence of RDR and the presence or absence of VTDR. Labeling was performed by retina specialists from fundus examination, according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Three convolutional neural networks (ResNet50) were trained with a transfer-learning procedure for assessing gradability and identifying VTDR and RDR. External validation was performed on 4 datasets spanning different geographical regions. Area under the receiver operating characteristic curve (AUROC) area under the precision-recall curve (AUPRC) sensitivity, specificity, and accuracy of the DL system in gradability assessment and detection of RDR and VTDR. For gradability assessment, the system achieved an AUROC of 0.923 (95% confidence interval [CI], 0.892-0.947), sensitivity of 86.5% (95% CI, 77.6-92.8), and specificity of 82.1% (95% CI, 77.3-86.2) for the primary validation dataset, and >0.82 AUROCs, >79.6% sensitivity, and >70.4% specificity for the geographical external validation datasets. For detecting RDR and VTDR, the AUROCs were 0.981 (95% CI, 0.977-0.984) and 0.966 (95% CI, 0.961-0.971), with sensitivities of 94.9% (95% CI, 92.3-97.9) and 87.2% (95% CI, 81.5-91.6), specificities of 95.1% (95% CI, 90.6-97.9) and 95.8% (95% CI, 93.3-97.6), and positive predictive values (PPVs) of 98.0% (95% CI, 96.1-99.0) and 91.1% (95% CI, 86.3-94.3) for the primary validation dataset, respectively. The AUROCs and accuracies for detecting both RDR and VTDR were >0.9% and >80%, respectively, for the geographical external validation datasets. The AUPRCs were >0.9, and sensitivities, specificities, and PPVs were >80% for the geographical external validation datasets for RDR and VTDR detection. The excellent performance achieved with this DL system for image quality assessment and detection of RDR and VTDR in UWF-SLO images highlights its potential as an efficient and effective diabetic retinopathy screening tool.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: IEEE
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.722
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: IEEE
Date: 11-2014
Publisher: Research Square Platform LLC
Date: 11-08-2022
DOI: 10.21203/RS.3.RS-1927944/V1
Abstract: Viral and host factors can shape SARS-CoV-2 within-host viral ersity and virus evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within in iduals. Here we analysed deep sequencing data from 2,146 SARS-CoV-2 s les with different viral lineages to describe the patterns of within-host ersity in different conditions, including vaccine-breakthrough infections. Variant of Concern (VOC) Alpha, Delta, and Omicron s les were found to have higher within-host nucleotide ersity while being under weaker purifying selection at full genome level compared to non-VOC SARS-CoV-2 viruses. Breakthrough Delta and Omicron infections in Comirnaty and CoronaVac vaccinated in iduals appeared to have higher within-host purifying selection at the full-genome and/or Spike gene levels. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 evolution. Our findings suggest that vaccination does not increase SARS-CoV-2 protein sequence space and may not facilitate emergence of more viral variants.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: IEEE
Date: 11-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2012
Publisher: Cold Spring Harbor Laboratory
Date: 26-08-2020
DOI: 10.1101/2020.08.26.267724
Abstract: Knowledge of the epitopes of SARS-CoV-2 that are targeted by T cells in convalescent patients is important for understanding T cell immunity against COVID-19. This information can aid the design, development and assessment of COVID-19 vaccines, and inform novel diagnostic technologies. Here we provide a unified description and meta-analysis of emerging data of SARS-CoV-2 T cell epitopes compiled from 15 independent studies of cohorts of convalescent COVID-19 patients. Our analysis demonstrates the broad ersity of T cell epitopes that have been collectively recorded for SARS-CoV-2, while also identifying a selected set of immunoprevalent epitopes that induced consistent T cell responses in multiple cohorts and in a large fraction of tested patients. The landscape of SARS-CoV-2 T cell epitopes that we describe can help guide future immunological studies, including those related to vaccines and diagnostics. A web-based platform has been developed to help complement these efforts.
Publisher: IEEE
Date: 06-2009
Publisher: Research Square Platform LLC
Date: 24-05-2023
DOI: 10.21203/RS.3.RS-2924528/V1
Abstract: Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.
Publisher: IEEE
Date: 04-2007
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
Publisher: IEEE
Date: 12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2008
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.859
Publisher: IEEE
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 04-2009
Publisher: IEEE
Date: 03-2008
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 06-2009
Publisher: IEEE
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: IEEE
Date: 2005
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: arXiv
Date: 2012
Publisher: arXiv
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2006
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 05-2010
Publisher: Elsevier BV
Date: 2022
Publisher: Statistica Sinica (Institute of Statistical Science)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: arXiv
Date: 2015
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 05-2013
Publisher: arXiv
Date: 2021
Publisher: IEEE
Date: 11-2009
Publisher: Public Library of Science (PLoS)
Date: 07-09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Research Square Platform LLC
Date: 27-01-2023
DOI: 10.21203/RS.3.RS-2487027/V1
Abstract: Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Using a sequence-based fitness landscape model that we validate using experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. We further demonstrate, using evolutionary modelling, that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.
Publisher: Cold Spring Harbor Laboratory
Date: 23-07-2019
DOI: 10.1101/711861
Abstract: Identifying the genetic drivers of adaptation is a necessary step in understanding the dynamics of rapidly evolving pathogens and cancer. However, signals of selection are obscured by the complex, stochastic nature of evolution. Pervasive effects of genetic linkage, including genetic hitchhiking and clonal interference between beneficial mutants, challenge our ability to distinguish the selective effect of in idual mutations. Here we describe a method to infer selection from genetic time series data that systematically resolves the confounding effects of genetic linkage. We applied our method to investigate patterns of selection in intrahost human immunodeficiency virus (HIV)-1 evolution, including a case in an in idual who develops broadly neutralizing antibodies (bnAbs). Most variants that arise are observed to have negligible effects on inferred selection at other sites, but a small minority of highly influential variants have strong and far-reaching effects. In particular, we found that accounting for linkage is crucial for estimating selection due to clonal interference between escape mutants and other variants that sweep rapidly through the population. We observed only modest selection for antibody escape, in contrast with strong selection for escape from CD8+ T cell responses. Weak selection for escape from antibody responses may facilitate bnAb development by ersifying the viral population. Our results provide a quantitative description of the evolution of HIV-1 in response to host immunity, including selection on the viral population that accompanies bnAb development. More broadly, our analysis argues for the importance of resolving linkage effects in studies of natural selection.
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 04-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: IEEE
Date: 06-2009
Publisher: IEEE
Date: 07-2006
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 04-2009
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer Science and Business Media LLC
Date: 02-08-2022
DOI: 10.1038/S41467-019-09819-1
Abstract: Isolation of broadly neutralizing human monoclonal antibodies (HmAbs) targeting the E2 glycoprotein of Hepatitis C virus (HCV) has sparked hope for effective vaccine development. Nonetheless, escape mutations have been reported. Ideally, a potent vaccine should elicit HmAbs that target regions of E2 that are most difficult to escape. Here, aimed at addressing this challenge, we develop a predictive in-silico evolutionary model for E2 that identifies one such region, a specific antigenic domain, making it an attractive target for a robust antibody response. Specific broadly neutralizing HmAbs that appear difficult to escape from are also identified. By providing a framework for identifying vulnerable regions of E2 and for assessing the potency of specific antibodies, our results can aid the rational design of an effective prophylactic HCV vaccine.
Publisher: arXiv
Date: 2012
Publisher: IEEE
Date: 18-07-2023
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2013
DOI: 10.1137/110843423
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: arXiv
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Publisher: IEEE
Date: 2008
Publisher: ACM
Date: 28-06-2010
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2010
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.890
Publisher: Springer Science and Business Media LLC
Date: 30-11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Springer Science and Business Media LLC
Date: 17-06-2020
Publisher: Oxford University Press (OUP)
Date: 21-09-2022
Abstract: Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from s led observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of in idual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic ersity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: arXiv
Date: 2011
Publisher: IEEE
Date: 11-2010
Publisher: arXiv
Date: 2021
Publisher: arXiv
Date: 2021
Publisher: IEEE
Date: 07-2006
Publisher: Oxford University Press (OUP)
Date: 18-12-2019
DOI: 10.1093/BIOINFORMATICS/BTZ925
Abstract: Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF–BML and encourage more widespread application to data in erse fields, we present a standalone cross-platform package of MPF–BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. The MPF–BML software is publicly available under the MIT License at hmedaq/MPF-BML-GUI. Supplementary data are available at Bioinformatics online.
Publisher: IEEE
Date: 06-2012
Publisher: arXiv
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: arXiv
Date: 2010
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2008
Publisher: IEEE
Date: 06-2013
Publisher: MDPI AG
Date: 25-02-2020
DOI: 10.3390/V12030254
Abstract: The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-1993
DOI: 10.1109/7.220934
Publisher: arXiv
Date: 2009
Publisher: arXiv
Date: 2010
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2009
Publisher: IEEE
Date: 11-2012
Publisher: Cold Spring Harbor Laboratory
Date: 24-05-2020
DOI: 10.1101/2020.05.23.111385
Abstract: We introduce COVIDep ( COVIDep.ust.hk ), a web-based platform that provides immune target recommendations for guiding SARS-CoV-2 vaccine development. COVIDep implements a protocol that pools together publicly-available genetic data for SARS-CoV-2 and epitope data for SARS-CoV to identify B cell and T cell epitopes that present potential immune targets for SARS-CoV-2. Correspondences between outputs of COVIDep and immune responses recorded in COVID-19 patients and preclinical vaccine trials are also indicated. The platform is user-friendly, flexible, and based on up-to-date data. It may help guide vaccine designs and associated experimental studies for SARS-CoV-2.
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: arXiv
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: arXiv
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2010
Publisher: arXiv
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.246
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-03-2020
DOI: 10.36227/TECHRXIV.11920419.V1
Abstract: Vaccines have saved more lives than any other medical intervention throughout human history by preventing the spread of infectious diseases. However, despite several decades of research, there is no effective vaccine against fast evolving viruses such as the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV). A confounding factor in the development of a HIV or HCV vaccine is that these viruses have a unique ability to make a lot of mutations in their genetic code. This enables them to escape the human immune system while retaining their ability to propagate infection. For developing a vaccine against such viruses, scientists are developing novel strategies which seek to target specific parts of the virus that are most vulnerable (i.e., where it is difficult for the virus to survive mutations) in order to induce a focused and potentially effective immune response. To determine the existence and location of such parts of HIV and HCV, initial studies have leveraged recently-available sequence data for these viruses, and looked for those positions in the genome for which the frequency of mutation was lowest. Unfortunately, vaccines based on such first-order statistics have not enjoyed much success, and there is increasing evidence suggesting that interactions between mutations is also important and must be considered when designing an effective vaccine against HIV and HCV. It is almost impossible to determine effects of interactions between all mutations experimentally as it requires performing billions of experiments. In this article, we explain how by leveraging virus sequence data, mutational interactions can be estimated using statistical techniques and incorporated in designing novel and potentially effective vaccine strategies against such fast-evolving viruses. br
Publisher: IEEE
Date: 03-2012
Publisher: Elsevier BV
Date: 2020
DOI: 10.2139/SSRN.3720371
Publisher: IEEE
Date: 07-2008
Publisher: arXiv
Date: 2020
Publisher: Cold Spring Harbor Laboratory
Date: 07-04-2023
DOI: 10.1101/2023.04.03.535505
Abstract: The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a noncovalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remains largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modelling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness, and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-03-2020
DOI: 10.36227/TECHRXIV.11920419
Abstract: Vaccines have saved more lives than any other medical intervention throughout human history by preventing the spread of infectious diseases. However, despite several decades of research, there is no effective vaccine against fast evolving viruses such as the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV). A confounding factor in the development of a HIV or HCV vaccine is that these viruses have a unique ability to make a lot of mutations in their genetic code. This enables them to escape the human immune system while retaining their ability to propagate infection. For developing a vaccine against such viruses, scientists are developing novel strategies which seek to target specific parts of the virus that are most vulnerable (i.e., where it is difficult for the virus to survive mutations) in order to induce a focused and potentially effective immune response. To determine the existence and location of such parts of HIV and HCV, initial studies have leveraged recently-available sequence data for these viruses, and looked for those positions in the genome for which the frequency of mutation was lowest. Unfortunately, vaccines based on such first-order statistics have not enjoyed much success, and there is increasing evidence suggesting that interactions between mutations is also important and must be considered when designing an effective vaccine against HIV and HCV. It is almost impossible to determine effects of interactions between all mutations experimentally as it requires performing billions of experiments. In this article, we explain how by leveraging virus sequence data, mutational interactions can be estimated using statistical techniques and incorporated in designing novel and potentially effective vaccine strategies against such fast-evolving viruses. br
Publisher: IEEE
Date: 06-2009
Publisher: arXiv
Date: 2014
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 11-2010
Publisher: Cold Spring Harbor Laboratory
Date: 25-04-2018
DOI: 10.1101/307033
Abstract: Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under erse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, in idual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies. HIV and HCV cause devastating infectious diseases for which no functional vaccine exists. A key problem is that while immune cells may induce in idual mutations that compromise viral fitness, this is typically restored through other “compensatory” mutations, leading to immune escape. These compensatory pathways are complicated and remain poorly understood. They do, however, leave co-evolutionary markers which may be inferred from measured sequence data. Here, by introducing a new robust statistical method, we demonstrated that the compensatory networks employed by both viruses exhibit a remarkably simple decomposition involving small and near-distinct groups of protein residues, with most groups having a clear association to biological function or structure. This provides insights that can be harnessed for the purpose of vaccine design.
Publisher: IEEE
Date: 06-2012
Publisher: arXiv
Date: 2014
Publisher: arXiv
Date: 2014
Publisher: IEEE
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Elsevier BV
Date: 04-2011
Publisher: Cold Spring Harbor Laboratory
Date: 03-02-2021
DOI: 10.1101/2021.02.02.429470
Abstract: Hepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection, cirrhosis and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the envelope glycoprotein 2 (E2) of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of subtype 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear to be escape-resistant against both subtypes 1a and 1b, providing directions for the design of HCV vaccines having cross-subtype protection.
Publisher: AIP Publishing
Date: 06-2013
DOI: 10.1063/1.4808081
Abstract: This paper gives an in-depth study of a multiple-antenna wireless communication scenario in which a weak signal received at an intermediate relay station is lified and then forwarded to the final destination. The key quantity determining system performance is the statistical properties of the signal-to-noise ratio (SNR) γ at the destination. Under certain assumptions on the encoding structure, recent work has characterized the SNR distribution through its moment generating function, in terms of a certain Hankel determinant generated via a deformed Laguerre weight. Here, we employ two different methods to describe the Hankel determinant. First, we make use of ladder operators satisfied by orthogonal polynomials to give an exact characterization in terms of a “double-time” Painlevé differential equation, which reduces to Painlevé V under certain limits. Second, we employ Dyson's Coulomb fluid method to derive a closed form approximation for the Hankel determinant. The two characterizations are used to derive closed-form expressions for the cumulants of γ, and to compute performance quantities of engineering interest.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Cold Spring Harbor Laboratory
Date: 04-02-2020
DOI: 10.1101/2020.02.03.933226
Abstract: The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the available SARS-CoV-2 sequences (as of 9 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.
Publisher: MDPI AG
Date: 02-01-2022
DOI: 10.3390/V14010079
Abstract: Omicron, the most recent SARS-CoV-2 variant of concern (VOC), harbours multiple mutations in the spike protein that were not observed in previous VOCs. Initial studies suggest Omicron to substantially reduce the neutralizing capability of antibodies induced from vaccines and previous infection. However, its effect on T cell responses remains to be determined. Here, we assess the effect of Omicron mutations on known T cell epitopes and report data suggesting T cell responses to remain broadly robust against this new variant.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 2004
Publisher: MDPI AG
Date: 03-09-2022
DOI: 10.3390/V14091960
Abstract: Beginning in May 2022, a novel cluster of monkeypox virus infections was detected in humans. This virus has spread rapidly to non-endemic countries, sparking global concern. Specific vaccines based on the vaccinia virus (VACV) have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variations that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates immune responses induced by VACV-based vaccines, including the currently available MVA-BN and ACAM2000 vaccines, to remain highly cross-reactive against the newly observed monkeypox viruses.
Publisher: arXiv
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Publisher: Proceedings of the National Academy of Sciences
Date: 08-01-2018
Abstract: An effective vaccine for HIV is still not available, although recent hope has emerged through the discovery of antibodies capable of neutralizing erse HIV strains. Nonetheless, there exist mutational pathways through which HIV can evade known broadly neutralizing antibody responses. An ideal vaccine would elicit broadly neutralizing antibodies that target parts of the virus’s spike proteins where mutations severely compromise the virus’s fitness. Here, we employ a computational approach that allows estimation of the fitness landscape (fitness as a function of sequence) of the polyprotein that comprises HIV’s spike. We validate the inferred landscape through comparisons with erse experimental measurements. The availability of this fitness landscape will aid the rational design of immunogens for effective vaccines.
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.854
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 2008
Publisher: arXiv
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 17-01-2020
DOI: 10.1038/S41467-019-14174-2
Abstract: Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations.
Publisher: arXiv
Date: 2014
Publisher: MDPI AG
Date: 31-03-2022
Abstract: Memory SARS-CoV-2-specific CD8+ T cell responses induced upon infection or COVID-19 vaccination have been important for protecting against severe COVID-19 disease while being largely robust against variants of concern (VOCs) observed so far. However, T cell immunity may be weakened by genetic mutations in future SARS-CoV-2 variants that lead to widespread T cell escape. The capacity for SARS-CoV-2 mutations to escape memory T cell responses requires comprehensive experimental investigation, though this is prohibited by the large number of SARS-CoV-2 mutations that have been observed. To guide targeted experimental studies, here we provide a screened list of potential SARS-CoV-2 T cell escape mutants. These mutants are identified as candidates for T cell escape as they lie within CD8+ T cell epitopes that are commonly targeted in in iduals and are predicted to abrogate HLA–peptide binding.
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.167
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: Wiley
Date: 10-11-2008
Publisher: IEEE
Date: 09-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Cold Spring Harbor Laboratory
Date: 23-06-2022
DOI: 10.1101/2022.06.23.497143
Abstract: Starting May 2022, a novel cluster of monkeypox virus infections was detected in humans. This has spread rapidly to non-endemic countries and sparked global concern. Vaccinia virus vaccines have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variation that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates vaccine-induced immune responses to remain highly cross-reactive against the newly observed monkeypox viruses.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
Publisher: Elsevier BV
Date: 04-2009
Publisher: arXiv
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 24-03-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 05-2014
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2007
Publisher: IEEE
Date: 11-2007
Publisher: American Society for Microbiology
Date: 07-2014
DOI: 10.1128/JVI.03812-13
Abstract: Chronic hepatitis C virus (HCV) infection is one of the leading causes of liver failure and liver cancer, affecting around 3% of the world's population. The extreme sequence variability of the virus resulting from error-prone replication has thwarted the discovery of a universal prophylactic vaccine. It is known that vigorous and multispecific cellular immune responses, involving both helper CD4 + and cytotoxic CD8 + T cells, are associated with the spontaneous clearance of acute HCV infection. Escape mutations in viral epitopes can, however, abrogate protective T-cell responses, leading to viral persistence and associated pathologies. Despite the propensity of the virus to mutate, there might still exist substitutions that incur a fitness cost. In this paper, we identify groups of coevolving residues within HCV nonstructural protein 3 (NS3) by analyzing erse sequences of this protein using ideas from random matrix theory and associated methods. Our analyses indicate that one of these groups comprises a large percentage of residues for which HCV appears to resist multiple simultaneous substitutions. Targeting multiple residues in this group through vaccine-induced immune responses should either lead to viral recognition or elicit escape substitutions that compromise viral fitness. Our predictions are supported by published clinical data, which suggested that immune genotypes associated with spontaneous clearance of HCV preferentially recognized and targeted this vulnerable group of residues. Moreover, mapping the sites of this group onto the available protein structure provided insight into its functional significance. An epitope-based immunogen is proposed as an alternative to the NS3 epitopes in the peptide-based vaccine IC41. IMPORTANCE Despite much experimental work on HCV, a thorough statistical study of the HCV sequences for the purpose of immunogen design was missing in the literature. Such a study is vital to identify epistatic couplings among residues that can provide useful insights for designing a potent vaccine. In this work, ideas from random matrix theory were applied to characterize the statistics of substitutions within the erse publicly available sequences of the genotype 1a HCV NS3 protein, leading to a group of sites for which HCV appears to resist simultaneous substitutions possibly due to deleterious effect on viral fitness. Our analysis leads to completely novel immunogen designs for HCV. In addition, the NS3 epitopes used in the recently proposed peptide-based vaccine IC41 were analyzed in the context of our framework. Our analysis predicts that alternative NS3 epitopes may be worth exploring as they might be more efficacious.
Start Date: 2023
End Date: 12-2025
Amount: $425,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 11-2021
End Date: 11-2025
Amount: $1,081,355.00
Funder: Australian Research Council
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