Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

Scientific Reports ◽

10.1038/s41598-020-78758-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Brandon Malone ◽

Boris Simovski ◽

Clément Moliné ◽

Jun Cheng ◽

Marius Gheorghe ◽

...

Keyword(s):

Artificial Intelligence ◽

Monte Carlo ◽

Cell Surface ◽

Antigen Presentation ◽

Human Population ◽

Host Immune System ◽

T Cell Epitopes ◽

Digital Twin ◽

Novel Coronavirus ◽

Global Population

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

10.21203/rs.3.pex-1006/v1 ◽

2020 ◽

Author(s):

Brandon Malone ◽

Boris Simovski ◽

Clément Moliné ◽

Jun Cheng ◽

Marius Gheorghe ◽

...

Keyword(s):

Artificial Intelligence ◽

Monte Carlo ◽

T Cell ◽

Monte Carlo Simulations ◽

Human Population ◽

T Cell Epitopes ◽

Maximum Coverage ◽

Hla Genotypes ◽

Significant Homology ◽

Global Population

Abstract This protocol predicts blueprints for vaccine design that contain a broad repertoire of T-cell epitopes optimized for the global population. The protocol first requires a screening of the SARS-CoV-2 proteome using immunogenicity predictors to generate comprehensive epitope maps. Then, these epitope maps are used as input to Monte Carlo simulations designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic. The epitope hotspots that share significant homology with proteins in the human proteome are removed to reduce the chance of inducing off-target autoimmune responses. Finally, a database of the actual HLA genotypes of citizens is used to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population. The approach identifies an optimal constellation of epitope hotspots that could provide maximum coverage in the human population.

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Faculty Opinions recommendation of Differential antigen presentation kinetics of CD8+ T-cell epitopes derived from the same viral protein.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1120752.576995 ◽

2008 ◽

Author(s):

Paul Goepfert ◽

Anju Bansal

Keyword(s):

T Cell ◽

Antigen Presentation ◽

Viral Protein ◽

Cd8 T Cell ◽

T Cell Epitopes ◽

Kinetics Of

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Prediction of Prophylactic Peptide Vaccine Candidates for Human Papillomavirus (HPV): An Immunoinformatics and Reverse Vaccinology Approach

Current Proteomics ◽

10.2174/1570164617999200505095802 ◽

2020 ◽

Vol 17 ◽

Author(s):

Mehreen Ismail ◽

Zureesha Sajid ◽

Amjad Ali ◽

Xiaogang Wu ◽

Syed Aun Muhammad ◽

...

Keyword(s):

T Cells ◽

Human Papillomavirus ◽

T Cell ◽

Binding Affinity ◽

Cd8 T Cells ◽

Reverse Vaccinology ◽

Host Immune System ◽

T Cell Epitopes ◽

Multiple Alleles ◽

Vaccine Candidates

Background: Human Papillomavirus (HPV) is responsible for substantial morbidity and mortality worldwide. We predicted immunogenic promiscuous monovalent and polyvalent T-cell epitopes from the polyprotein of the Human Papillomavirus (HPV) using a range of bioinformatics tools and servers. Methods: We used immunoinformatics and reverse vaccinology-based approaches to design prophylactic peptides by antigenicity analysis, Tcell epitopes prediction, proteasomal and conservancy evaluation, host-pathogen protein interactions, and in silico binding affinity analysis. Results: We found two early proteins (E2 and E6) and two late proteins (L1 and L2) of HPV as potential vaccine candidates. Of these proteins (E2, E6, L1 & L2), 2-epitopes of each candidate protein for multiple alleles of MHC class I and II bearing significant binding affinity (>-6.0 kcal/mole). These potential epitopes for CD4+ and CD8+ T-cells were also linked to design polyvalent construct using GPGPG linkers. Cholera toxin B and mycobacterial heparin-binding hemagglutinin adjuvant with a molecular weight of 12.5 and 18.5 kDa were used for epitopes of CD4+ and CD8+ T-cells respectively. The molecular docking indicated the optimum binding affinity of HPV peptides with MHC molecules. This interaction showed that our predicted vaccine candidates are suitable to trigger the host immune system to prevent HPV infections. Conclusion: The predicted conserved T-cell epitopes would contribute to the imminent design of HPV vaccine candidates, which will be able to induce a broad range of immune-responses in a heterogeneous HLA population.

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A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

International Journal of Health Services ◽

10.1177/00207314211017469 ◽

2021 ◽

pp. 002073142110174

Author(s):

Md Mijanur Rahman ◽

Fatema Khatun ◽

Ashik Uzzaman ◽

Sadia Islam Sami ◽

Md Al-Amin Bhuiyan ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Language Processing ◽

Probabilistic Models ◽

Health Care Systems ◽

Learning Approaches ◽

Care Systems ◽

Novel Coronavirus ◽

Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽

10.3390/en14082338 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2338

Author(s):

Sofia Agostinelli ◽

Fabrizio Cumo ◽

Giambattista Guidi ◽

Claudio Tomazzoli

Keyword(s):

Artificial Intelligence ◽

Renewable Energy ◽

Energy Management ◽

Three Dimensional ◽

Cost Effective ◽

Automation System ◽

Climate Conditions ◽

Digital Twin ◽

Building Energy Management ◽

Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

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A Graph Based Monte Carlo Simulation supporting a Digital Twin for the Curatorial Management of Excavation and Demolition Material Flows

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.127453 ◽

2021 ◽

pp. 127453

Author(s):

Simon Züst ◽

Rainer Züst ◽

Viturin Züst ◽

Shaun West ◽

Oliver Stoll ◽

...

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Material Flows ◽

Digital Twin

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ACE2 Nascence, trafficking, and SARS-CoV-2 pathogenesis: the saga continues

Human Genomics ◽

10.1186/s40246-021-00304-9 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

Sally Badawi ◽

Bassam R. Ali

Keyword(s):

Cell Surface ◽

Angiotensin Converting Enzyme ◽

The Novel ◽

Converting Enzyme ◽

Post Translational Modifications ◽

Angiotensin Converting Enzyme 2 ◽

Viral Attachment ◽

Novel Coronavirus ◽

Effective Medication ◽

Potential Use

AbstractWith the emergence of the novel coronavirus SARS-CoV-2 since December 2019, more than 65 million cases have been reported worldwide. This virus has shown high infectivity and severe symptoms in some cases, leading to over 1.5 million deaths globally. Despite the collaborative and concerted research efforts that have been made, no effective medication for COVID-19 (coronavirus disease-2019) is currently available. SARS-CoV-2 uses the angiotensin-converting enzyme 2 (ACE2) as an initial mediator for viral attachment and host cell invasion. ACE2 is widely distributed in the human tissues including the cell surface of lung cells which represent the primary site of the infection. Inhibiting or reducing cell surface availability of ACE2 represents a promising therapy for tackling COVID-19. In this context, most ACE2–based therapeutic strategies have aimed to tackle the virus through the use of angiotensin-converting enzyme (ACE) inhibitors or neutralizing the virus by exogenous administration of ACE2, which does not directly aim to reduce its membrane availability. However, through this review, we present a different perspective focusing on the subcellular localization and trafficking of ACE2. Membrane targeting of ACE2, and shedding and cellular trafficking pathways including the internalization are not well elucidated in literature. Therefore, we hereby present an overview of the fate of newly synthesized ACE2, its post translational modifications, and what is known of its trafficking pathways. In addition, we highlight the possibility that some of the identified ACE2 missense variants might affect its trafficking efficiency and localization and hence may explain some of the observed variable severity of SARS-CoV-2 infections. Moreover, an extensive understanding of these processes is necessarily required to evaluate the potential use of ACE2 as a credible therapeutic target.

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