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Artikel ; Online: Vaccine Design by Reverse Vaccinology and Machine Learning.

Ong, Edison / He, Yongqun

Methods in molecular biology (Clifton, N.J.)

2021 Band 2414, Seite(n) 1–16

Abstract: Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction ... ...

Abstract	Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data. Using a benchmark dataset, Vaxign-ML showed superior performance in comparison to existing open-source RV tools. Vaxign-ML is also implemented within the web-based Vaxign platform to support easy and intuitive access. Vaxign-ML is also available as a command-based software package for more advanced and customizable vaccine antigen prediction. Both Vaxign and Vaxign-ML have been applied to predict SARS-CoV-2 (cause of COVID-19) and Brucella vaccine antigens to demonstrate the integrative approach to analyze and select vaccine candidates using the Vaxign platform.
Mesh-Begriff(e)	Brucella Vaccine ; COVID-19 ; COVID-19 Vaccines ; Computational Biology ; Humans ; Machine Learning ; Vaccines ; Vaccinology
Chemische Substanzen	Brucella Vaccine ; COVID-19 Vaccines ; Vaccines
Sprache	Englisch
Erscheinungsdatum	2021-11-13
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1900-1_1
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: mtx-COBRA: Subcellular localization prediction for bacterial proteins.

Arora, Isha / Kummer, Arkadij / Zhou, Hao / Gadjeva, Mihaela / Ma, Eric / Chuang, Gwo-Yu / Ong, Edison

Computers in biology and medicine

2024 Band 171, Seite(n) 108114

Abstract: Background: Bacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are ... ...

Abstract	Background: Bacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and can induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets. Method: We present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Meta's protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset. Results: Using benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb. Conclusions: mtx-COBRA provides an accessible pipeline that can more efficiently classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.
Mesh-Begriff(e)	Bacterial Proteins/chemistry ; Software ; Bacteria ; Amino Acid Sequence ; Vaccines ; Computational Biology/methods
Chemische Substanzen	Bacterial Proteins ; Vaccines
Sprache	Englisch
Erscheinungsdatum	2024-02-10
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2024.108114
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Identification of

Teahan, Blaine / Ong, Edison / Yang, Zhenhua

Vaccines

2021 Band 9, Heft 10

Abstract: Tuberculosis (TB) is the leading cause of death of any single infectious agent, having led to 1.4 million deaths in 2019 alone. Moreover, an estimated one-quarter of the global population is latently infected ... ...

Abstract	Tuberculosis (TB) is the leading cause of death of any single infectious agent, having led to 1.4 million deaths in 2019 alone. Moreover, an estimated one-quarter of the global population is latently infected with
Sprache	Englisch
Erscheinungsdatum	2021-09-28
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines9101098
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Epitope promiscuity and population coverage of Mycobacterium tuberculosis protein antigens in current subunit vaccines under development.

Ong, Edison / He, Yongqun / Yang, Zhenhua

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases

2020 Band 80, Seite(n) 104186

Abstract: Tuberculosis (TB) is the leading infectious cause of death worldwide and claimed over 1.6 million lives in 2017. Furthermore, one-third of the world population is estimated to be latently infected with Mycobacterium tuberculosis (MTB). A safe and ... ...

Abstract	Tuberculosis (TB) is the leading infectious cause of death worldwide and claimed over 1.6 million lives in 2017. Furthermore, one-third of the world population is estimated to be latently infected with Mycobacterium tuberculosis (MTB). A safe and effective MTB vaccine that can prevent both the primary infection and the reactivation of latent tuberculosis infection (LTBI), and that can protect against all forms of TB in adults and adolescents is urgently needed. In this study, using computational approaches, we predicted the capacity of the epitopes to be presented by the HLA molecules for ten MTB protein antigens (Mtb39a, Mtb32a, Ag85B, ESAT-6, TB10.4, Rv2660, Rv2608, Rv3619, Rv3620, and Rv1813) constituting five MTB subunit vaccines (M72, H1, H4, H56, and ID93) that are currently in clinical trials. We also assessed the promiscuity of the predicted epitopes based on a reference set of alleles and supertype alleles, and estimated the population coverage of the ten antigens in three high TB burden countries (China, India, and South Africa). Among the ten antigens evaluated, Rv2608 was found to have the highest number of promiscuous epitopes predicted to bind the most MHC-I and MHC-II supertype alleles, highest predicted immunogenicity, and the broadest population coverage in three high burden countries. Between the two latency-related antigens (Rv1813 and Rv2660), Rv1813 was predicted to have a better epitope diversity and promiscuity, immunogenicity, and population coverage. As a result, the ID93 vaccine consisted of Rv2608, Rv1813, Rv3619, and Rv3620 was predicted to have the best potential for preventing both active and latent TB infection. Our results highlighted the importance and usefulness of a systematic and comprehensive assessment of protein antigens using computational approaches in MTB vaccine development.
Mesh-Begriff(e)	Alleles ; Amino Acid Sequence ; Antigens, Bacterial/chemistry ; Antigens, Bacterial/genetics ; Antigens, Bacterial/immunology ; Epitopes/chemistry ; Epitopes/genetics ; Epitopes/immunology ; Histocompatibility Antigens Class I/genetics ; Histocompatibility Antigens Class I/immunology ; Humans ; Immunogenicity, Vaccine/genetics ; Latent Tuberculosis/prevention & control ; Mycobacterium tuberculosis/genetics ; Mycobacterium tuberculosis/immunology ; Tuberculosis/prevention & control ; Tuberculosis Vaccines/immunology ; Vaccination Coverage ; Vaccines, Subunit/immunology
Chemische Substanzen	Antigens, Bacterial ; Epitopes ; Histocompatibility Antigens Class I ; Tuberculosis Vaccines ; Vaccines, Subunit ; Mycobacterium tuberculosis antigens (144058-44-6)
Sprache	Englisch
Erscheinungsdatum	2020-01-08
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	2037068-4
ISSN	1567-7257 ; 1567-1348
ISSN (online)	1567-7257
ISSN	1567-1348
DOI	10.1016/j.meegid.2020.104186
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.

Huffman, Anthony / Ong, Edison / Hur, Junguk / D'Mello, Adonis / Tettelin, Hervé / He, Yongqun

Briefings in bioinformatics

2022 Band 23, Heft 4

Abstract: Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational ... ...

Abstract	Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
Mesh-Begriff(e)	COVID-19/prevention & control ; COVID-19 Vaccines ; Data Mining ; Humans ; Machine Learning ; Vaccines/chemistry ; Vaccines/genetics ; Vaccinology/methods
Chemische Substanzen	COVID-19 Vaccines ; Vaccines
Sprache	Englisch
Erscheinungsdatum	2022-06-01
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac190
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning.

Ong, Edison / Wong, Mei U / Huffman, Anthony / He, Yongqun

bioRxiv : the preprint server for biology

2020

Abstract: To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, ...

Abstract	To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane protein, have been tested for vaccine development against SARS and MERS. We further used the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. The N protein was found to be conserved in the more pathogenic strains (SARS/MERS/COVID-19), but not in the other human coronaviruses that mostly cause mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10) were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and linear B-cell epitopes localized in specific locations and functional domains of the protein. Our predicted vaccine targets provide new strategies for effective and safe COVID-19 vaccine development.
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-03-21
Erscheinungsland	United States
Dokumenttyp	Preprint
DOI	10.1101/2020.03.20.000141
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering.

Ong, Edison / Huang, Xiaoqiang / Pearce, Robin / Zhang, Yang / He, Yongqun

bioRxiv : the preprint server for biology

2020

Abstract: The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of ... ...

Abstract	The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of these vaccines is the SARS-CoV-2 spike (S) protein, and many studies utilized structural vaccinology techniques to either stabilize the protein or fix the receptor-binding domain at certain states. In this study, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants without perturbing the surface conformation and B cell epitopes of the S protein. We then evaluated the mutated S protein candidates based on predicted MHC-II T cell promiscuous epitopes as well as the epitopes' similarity to human peptides. The presented strategy aims to improve the S protein's immunogenicity and antigenicity by inducing stronger CD4 T cell response while maintaining the protein's native structure and function. The top EvoDesign S protein candidate (Design-10705) recovered 31 out of 32 MHC-II T cell promiscuous epitopes in the native S protein, in which two epitopes were present in all seven human coronaviruses. This newly designed S protein also introduced nine new MHC-II T cell promiscuous epitopes and showed high structural similarity to its native conformation. The proposed structural vaccinology method provides an avenue to rationally design the antigen's structure with increased immunogenicity, which could be applied to the rational design of new COVID-19 vaccine candidates.
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-08-14
Erscheinungsland	United States
Dokumenttyp	Preprint
DOI	10.1101/2020.08.14.251496
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

Ong, Edison / Wong, Mei U / Huffman, Anthony / He, Yongqun

Frontiers in immunology

2020 Band 11, Seite(n) 1581

Abstract	To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.
Mesh-Begriff(e)	Animals ; Betacoronavirus/genetics ; Betacoronavirus/immunology ; COVID-19 ; COVID-19 Vaccines ; Coronavirus Infections/epidemiology ; Coronavirus Infections/genetics ; Coronavirus Infections/immunology ; Coronavirus Infections/prevention & control ; Epitopes, B-Lymphocyte/genetics ; Epitopes, B-Lymphocyte/immunology ; Humans ; Immunogenicity, Vaccine ; Machine Learning ; Middle East Respiratory Syndrome Coronavirus/genetics ; Middle East Respiratory Syndrome Coronavirus/immunology ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/genetics ; Pneumonia, Viral/immunology ; Pneumonia, Viral/prevention & control ; SARS-CoV-2 ; Viral Proteins/genetics ; Viral Proteins/immunology ; Viral Vaccines/genetics ; Viral Vaccines/immunology
Chemische Substanzen	COVID-19 Vaccines ; Epitopes, B-Lymphocyte ; Viral Proteins ; Viral Vaccines
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-07-03
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Systematic Review
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2020.01581
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering.

Ong, Edison / Huang, Xiaoqiang / Pearce, Robin / Zhang, Yang / He, Yongqun

Computational and structural biotechnology journal

2020 Band 19, Seite(n) 518–529

Abstract: The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the ... ...

Abstract	The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.
Sprache	Englisch
Erscheinungsdatum	2020-12-31
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2020.12.039
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia.

Ong, Edison / He, Yongqun

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

2016 Band 2016, Seite(n) 65–74

Abstract: Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To ... ...

Abstract	Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features.
Sprache	Englisch
Erscheinungsdatum	2016-07-20
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2676378-3
ISSN	2153-4063
ISSN	2153-4063
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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