Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning

Ong, Edison; Cooke, Michael F.; Huffman, Anthony; Xiang, Zuoshuang; Wong, May; Wang, Haihe; Seetharaman, Meenakshi; Valdez, Ninotchka; He, Yongqun

doi:10.1093/nar/gkab279

Cited by 63 publications

(38 citation statements)

References 38 publications

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“…As described previously by Ong et al, Vaxign-ML used 397 bacterial PAgs with at least one experimental evidence of protection (e.g., in an animal challenge assay) to train an extreme gradient boosting model [23]. With a recommended protegenicity score threshold, Vaxign-ML achieved the highest performance with 0.96 weighted F1-score in a nested five-fold cross-validation and outperformed other existing web-based RV tools [23,25].…”

Section: Methodsmentioning

confidence: 95%

Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

2021

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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.

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Section: Methodsmentioning

confidence: 95%

Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

2021

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“…(22) In contrast, Vaxign2, Vaxign-ML and Vaxijen3.0 although developed as a machine learning (ML) model; which has better results; their training sets were exclusively with bacterial proteins. (32,33,34) Therefore, they are not to be trusted in regards of antigenicity of a protozoa parasite.…”

Section: Discussionmentioning

confidence: 99%

Antigenicity and adhesiveness of a Plasmodium vivax VIR-E protein from Brazilian isolates

Schappo

Bittencourt

Bertolla

et al. 2021

Mem. Inst. Oswaldo Cruz

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BACKGROUND Plasmodium vivax, the major cause of malaria in Latin America, has a large subtelomeric multigene family called vir. In the P. vivax genome, about 20% of its sequences are vir genes. Vir antigens are grouped in subfamilies according to their sequence similarities and have been shown to have distinct roles and subcellular locations. However, little is known about vir subfamilies, especially when comes to their functions. OBJECTIVE To evaluate the diversity, antigenicity, and adhesiveness of Plasmodium vivax VIR-E.METHODS Vir-E genes were amplified from six P. vivax isolates from Manaus, North of Brazil. The presence of naturally acquired antibodies to recombinant PvBrVIR-E and PvAMA-1 was evaluated by ELISA. Binding capacity of recombinant PvBrVIR-E was assessed by adhesion assay to CHO-ICAM1 cells.FINDINGS Despite vir-E sequence diversity, among those identified sequences, a representative one was chosen to be expressed as recombinant protein. The presence of IgM or IgG antibodies to PvBrVIR-E was detected in 23.75% of the study population while the presence of IgG antibodies to PvAMA-1 antigen was 66.25% in the same population. PvBrVIR-E was adhesive to CHO-ICAM1.MAIN CONCLUSIONS PvBrVIR-E was antigenic and adhesive to CHO-ICAM1.

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“…The ProtParam bioinformatics service [ 35 ] for the physicochemical characteristics of the sequence was used; the web-based vaccine target design program Vaxign 2.0 [ 36 ] in combination with the Immune Epitope Database (IEDB) [ 37 ] was employed to evaluate the immunogenic properties as well as the reference human leukocyte antigens (HLA) that could recognize the consensus sequence, and for the prediction of Major Histocompatibility Complex I and II (MHC-I and II) epitopes mainly associated with the Latin American population, selecting only epitopes with a p -value ≤ 0.01 for both cases. The selection and validation of epitopes were first performed by their representativeness in the HLA’s supertypes, and then by the presence of proteasomal cleavage sites for the case of MHC-I through the NetChop 3.1 platform [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Consensus Enolase of Trypanosoma Cruzi: Evaluation of Their Immunogenic Properties Using a Bioinformatics Approach

Díaz-Hernández

González-Vázquez

Arce-Fonseca

et al. 2022

Life

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There is currently no vaccine against American trypanosomiasis, caused by the parasite Trypanosoma cruzi. This is due to the genomic variation observed in the six DTUs of T. cruzi. This work aims to propose a consensus sequence of the enolase protein from different strains of T. cruzi and mainly evaluate its immunogenic properties at the bioinformatic level. From specialized databases, 15 sequences of the enolase gene were aligned to obtain a consensus sequence, where this sequence was modeled and then evaluated and validated through different bioinformatic programs to learn their immunogenic potential. Finally, chimeric peptides were designed with the most representative epitopes. The results showed high immunogenic potential with six epitopes for MHC-I, and seven epitopes for MHC-II, all of which were highly representative of the enolase present in strains from the American continent as well as five epitopes for B cells. Regarding the computational modeling, molecular docking with Toll-like receptors showed a high affinity and low constant of dissociation, which could lead to an innate-type immune response that helps to eliminate the parasite. In conclusion, the consensus sequence proposed for enolase is capable of providing an ideal immune response; however, the experimental evaluation of this enolase consensus and their chimeric peptides should be a high priority to develop a vaccine against Chagas disease.

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Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning

Cited by 63 publications

References 38 publications

Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

Antigenicity and adhesiveness of a Plasmodium vivax VIR-E protein from Brazilian isolates

Consensus Enolase of Trypanosoma Cruzi: Evaluation of Their Immunogenic Properties Using a Bioinformatics Approach

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