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

Ong, Edison / He, Yongqun

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

2021 Volume 2414, Page(s) 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 term(s)	Brucella Vaccine ; COVID-19 ; COVID-19 Vaccines ; Computational Biology ; Humans ; Machine Learning ; Vaccines ; Vaccinology
Chemical Substances	Brucella Vaccine ; COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2021-11-13
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1900-1_1
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Article: Identification of

Teahan, Blaine / Ong, Edison / Yang, Zhenhua

Vaccines

2021 Volume 9, Issue 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
Language	English
Publishing date	2021-09-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines9101098
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Article ; 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 Volume 171, Page(s) 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 term(s)	Bacterial Proteins/chemistry ; Software ; Bacteria ; Amino Acid Sequence ; Vaccines ; Computational Biology/methods
Chemical Substances	Bacterial Proteins ; Vaccines
Language	English
Publishing date	2024-02-10
Publishing country	United States
Document type	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
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Article ; Online: Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

Blaine Teahan / Edison Ong / Zhenhua Yang

Vaccines, Vol 9, Iss 1098, p

2021 Volume 1098

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 Mycobacterium tuberculosis (MTB), presenting a huge pool of potential future disease. Nonetheless, the only currently licensed TB vaccine fails to prevent the activation of latent TB infections (LTBI). These facts together illustrate the desperate need for a more effective TB vaccine strategy that can prevent both primary infection and the activation of LTBI. In this study, we employed a machine learning-based reverse vaccinology approach to predict the likelihood that each protein within the proteome of MTB laboratory reference strain H37Rv would be a protective antigen (PAg). The proteins predicted most likely to be a PAg were assessed for their belonging to a protein family of previously established PAgs, the relevance of their biological processes to MTB virulence and latency, and finally the immunogenic potential that they may provide in terms of the number of promiscuous epitopes within each. This study led to the identification of 16 proteins with the greatest vaccine potential for further in vitro and in vivo studies. It also demonstrates the value of computational methods in vaccine development.
Keywords	tuberculosis ; vaccines ; protective antigens ; machine learning ; reverse vaccinology ; Medicine ; R
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
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Article ; 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 Volume 23, Issue 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 term(s)	COVID-19/prevention & control ; COVID-19 Vaccines ; Data Mining ; Humans ; Machine Learning ; Vaccines/chemistry ; Vaccines/genetics ; Vaccinology/methods
Chemical Substances	COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2022-06-01
Publishing country	England
Document type	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
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Article ; 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 Volume 80, Page(s) 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 term(s)	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
Chemical Substances	Antigens, Bacterial ; Epitopes ; Histocompatibility Antigens Class I ; Tuberculosis Vaccines ; Vaccines, Subunit ; Mycobacterium tuberculosis antigens (144058-44-6)
Language	English
Publishing date	2020-01-08
Publishing country	Netherlands
Document type	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
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Article: 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.
Keywords	covid19
Language	English
Publishing date	2020-03-21
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.03.20.000141
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Article: 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.
Keywords	covid19
Language	English
Publishing date	2020-08-14
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.08.14.251496
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Article: 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 Volume 19, Page(s) 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.
Language	English
Publishing date	2020-12-31
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2020.12.039
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Article: Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients.

Wang, Haihe / Ong, Edison / Kao, John Y / Sun, Duxin / He, Yongqun

Frontiers in microbiology

2021 Volume 12, Page(s) 633732

Abstract: Alterations in the gut microbiome have been associated with various human diseases. Most existing gut microbiome studies stopped at the stage of identifying microbial alterations between diseased or healthy conditions. As inspired by reverse vaccinology ( ...

Abstract	Alterations in the gut microbiome have been associated with various human diseases. Most existing gut microbiome studies stopped at the stage of identifying microbial alterations between diseased or healthy conditions. As inspired by reverse vaccinology (RV), we developed a new strategy called Reverse Microbiomics (RM) that turns this process around: based on the identified microbial alternations, reverse-predicting the molecular mechanisms underlying the disease and microbial alternations. Our RM methodology starts by identifying significantly altered microbiota profiles, performing bioinformatics analysis on the proteomes of the microbiota identified, and finally predicting potential virulence or protective factors relevant to a microbiome-associated disease. As a use case study, this reverse methodology was applied to study the molecular pathogenesis of rheumatoid arthritis (RA), a common autoimmune and inflammatory disease. Those bacteria differentially associated with RA were first identified and annotated from published data and then modeled and classified using the Ontology of Host-Microbiome Interactions (OHMI). Our study identified 14 species increased and 9 species depleted in the gut microbiota of RA patients. Vaxign was used to comparatively analyze 15 genome sequences of the two pairs of species: Gram-negative
Language	English
Publishing date	2021-02-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2021.633732
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