In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Yuan, Lifang; Li, Xu; Li, Minchao; Bi, Rongjun; Li, Yingrui; Song, Jiaping; Li, Wei; Yan, Mingchen; Luo, Huanle; Sun, Caijun; Shu, Yuelong

doi:10.1016/j.ijbiomac.2023.128071

Cited by 3 publications

(4 citation statements)

References 90 publications

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“…We tested AutoEpiCollect's pan-cancer vaccine design on common cancers containing prevalent PIK3CA mutations. Previous cancer vaccines targeting the PIK3CA gene primarily target small sets of missense mutations, thus reducing the scope and efficiency of the vaccine [1][2][3][4][5]. Therefore, no clinical trials have been conducted with pan-cancer vaccines targeting the PIK3CA gene.…”

Section: Discussionmentioning

confidence: 99%

“…The development of these vaccine designs has been steadily increasing due to the ability of vaccines to safely boost the immune system's innate defense mechanisms against a broad number of diseases and pathogens. Some of the latest findings involving in silico vaccine designs include potential methods of protection against different viruses, bacteria, and some single-celled organisms [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Samudrala,

Dhaveji,

Savsani

et al. 2024

Bioengineering

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Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect’s vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Samudrala,

Dhaveji,

Savsani

et al. 2024

Bioengineering

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“…Professor Yuelong Shu, director of the National institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, reported his team’s latest achievements on novel strategies of universe influenza vaccines, especially that a kind of mosaic virus-like particles (VLPs) immunization elicited robust cross-reactive neutralizing antibody responses against H1N1 and H3N2 viruses in mice. 18 , 19 …”

Section: Plenary Session Reportmentioning

confidence: 99%

The 2 ^nd China Vaccinology Integrated Innovation & Teaching Development Conference: Promoting the construction of vaccinology discipline system

Chen,

Shu,

Zheng

et al. 2024

Human Vaccines & Immunotherapeutics

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The 2 nd China Vaccinology Integrated Innovation & Teaching Development Conference was held in Sun Yat-sen University, Shenzhen, 18–19, November 2023. Over 200 participants in the field of Vaccinology gathered together to address challenges and issues relevant to vaccine education and training courses, research, and public health programs in China. The conference themed “Promoting the Integrated and Innovative Development of Vaccinology through Collective Efforts.” The conference was organized by the China Association of Vaccine (CAV) and hosted by Vaccinology Education Professional Committee of CAV, and School of Public Health (Shenzhen), Sun Yat-sen University. Other partners included the Medical Virology Branch of the Chinese Medical Association, the editorial committee of the Chinese Journal of Preventive Medicine, Human Vaccines & Immunotherapeutics, and the People’s Medical Publishing House. The 1 st conference was held in Hangzhou, in October 2020.

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“…Peptide-based vaccines are synthesized in vitro by utilizing known immunogenic amino acid sequences, generally from B-cell or T-cell epitopes, and they have recently gained attention due to their ability to trigger both humoral and T-cell immune responses [ 23 , 24 , 25 ]. They have been used for the development of vaccines against various infectious diseases, including Malaria, influenza, HIV-1, SARS-CoV-2, and hepatitis C virus [ 23 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Mixture of T-Cell Epitope Peptides Derived from Human Respiratory Syncytial Virus F Protein Conferred Protection in DR1-TCR Tg Mice

Guo,

Song,

et al. 2024

Vaccines

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Human respiratory syncytial virus (HRSV) poses a significant disease burden on global health. To date, two vaccines that primarily induce humoral immunity to prevent HRSV infection have been approved, whereas vaccines that primarily induce T-cell immunity have not yet been well-represented. To address this gap, 25 predicted T-cell epitope peptides derived from the HRSV fusion protein with high human leukocyte antigen (HLA) binding potential were synthesized, and their ability to be recognized by PBMC from previously infected HRSV cases was assessed using an ELISpot assay. Finally, nine T-cell epitope peptides were selected, each of which was recognized by at least 20% of different donors’ PBMC as potential vaccine candidates to prevent HRSV infection. The protective efficacy of F-9PV, a combination of nine peptides along with CpG-ODN and aluminum phosphate (Al) adjuvants, was validated in both HLA-humanized mice (DR1-TCR transgenic mice, Tg mice) and wild-type (WT) mice. The results show that F-9PV significantly enhanced protection against viral challenge as evidenced by reductions in viral load and pathological lesions in mice lungs. In addition, F-9PV elicits robust Th1-biased response, thereby mitigating the potential safety risk of Th2-induced respiratory disease during HRSV infection. Compared to WT mice, the F-9PV mice exhibited superior protection and immunogenicity in Tg mice, underscoring the specificity for human HLA. Overall, our results demonstrate that T-cell epitope peptides provide protection against HRSV infection in animal models even in the absence of neutralizing antibodies, indicating the feasibility of developing an HRSV T-cell epitope peptide-based vaccine.

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In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Cited by 3 publications

References 90 publications

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

The 2 ^nd China Vaccinology Integrated Innovation & Teaching Development Conference: Promoting the construction of vaccinology discipline system

A Mixture of T-Cell Epitope Peptides Derived from Human Respiratory Syncytial Virus F Protein Conferred Protection in DR1-TCR Tg Mice

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In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Cited by 3 publications

References 90 publications

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

The 2 nd China Vaccinology Integrated Innovation & Teaching Development Conference: Promoting the construction of vaccinology discipline system

A Mixture of T-Cell Epitope Peptides Derived from Human Respiratory Syncytial Virus F Protein Conferred Protection in DR1-TCR Tg Mice

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Product

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The 2 ^nd China Vaccinology Integrated Innovation & Teaching Development Conference: Promoting the construction of vaccinology discipline system