Vaccine Peptide

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 25347 Experts worldwide ranked by ideXlab platform

Ferdinand Njume Ngale - One of the best experts on this subject based on the ideXlab platform.

  • in silico design of a multi epitope Vaccine candidate against onchocerciasis and related filarial diseases
    Scientific Reports, 2019
    Co-Authors: Robert Adamu Shey, Stephen Mbigha Ghogomu, Kevin K Esoh, Neba Derrick Nebangwa, Cabirou Mounchili Shintouo, Nkemngo Francis Nongley, Bertha Fru Asa, Ferdinand Njume Ngale
    Abstract:

    Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic Vaccine targeting the infective L3 and microfilaria stages of the parasite’s life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a Vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit Vaccine Peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel Vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric Vaccine Peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative Peptide demonstrated antigenicity superior to current Vaccine candidates.

Robert Adamu Shey - One of the best experts on this subject based on the ideXlab platform.

  • in silico design of a multi epitope Vaccine candidate against onchocerciasis and related filarial diseases
    Scientific Reports, 2019
    Co-Authors: Robert Adamu Shey, Stephen Mbigha Ghogomu, Kevin K Esoh, Neba Derrick Nebangwa, Cabirou Mounchili Shintouo, Nkemngo Francis Nongley, Bertha Fru Asa, Ferdinand Njume Ngale
    Abstract:

    Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic Vaccine targeting the infective L3 and microfilaria stages of the parasite’s life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a Vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit Vaccine Peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel Vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric Vaccine Peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative Peptide demonstrated antigenicity superior to current Vaccine candidates.

David K Gifford - One of the best experts on this subject based on the ideXlab platform.

  • predicted cellular immunity population coverage gaps for sars cov 2 subunit Vaccines and their augmentation by compact joint sets
    bioRxiv, 2020
    Co-Authors: Ge Liu, Brandon Carter, David K Gifford
    Abstract:

    Abstract Subunit Vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen Peptides for cellular immunity based memory. We find that SARS-CoV-2 subunit Peptides may not be robustly displayed by the Major Histocompatibility Complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit Vaccines that adds a small number of Peptides to a Vaccine to improve the population coverage of pathogen Peptide display. We augment a subunit Vaccine by selecting additional pathogen Peptides to maximize the total number of Vaccine Peptide hits against the distribution of MHC haplotypes in a population. For each subunit we design independent MHC class I and MHC class II Peptide sets for augmentation, and alternatively design a combined set of Peptides for MHC class I and class II display. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap. We predict that a SARS-CoV-2 receptor binding domain subunit Vaccine will have fewer than six Peptide-HLA hits with ≤ 50 nM binding affinity per individual in 51.31% (class I) and 32.99% (class II) of the population, and with augmentation, the uncovered population is predicted to be reduced to 0.54% (class I) and 1.46% (class II). We find that a joint set of pathogen Peptides for MHC class I and class II display is predicted to produce a more compact Vaccine design than using independent sets for MHC class I and class II. We provide an open source implementation of our design methods (OptiVax), Vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax/tree/master/augmentation.

  • computationally optimized sars cov 2 mhc class i and ii Vaccine formulations predicted to target human haplotype distributions
    Cell systems, 2020
    Co-Authors: Brandon Carter, Trenton Bricken, Siddhartha Jain, Mathias Viard, Mary Carrington, David K Gifford
    Abstract:

    We present a combinatorial machine learning method to evaluate and optimize Peptide Vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of Vaccine Peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I Vaccine formulations provide 93.21% predicted population coverage with at least five Vaccine Peptide-HLA average hits per person (≥ 1 Peptide: 99.91%) with all Vaccine Peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II Vaccine formulations provide 97.21% predicted coverage with at least five Vaccine Peptide-HLA average hits per person with all Peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), Vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

  • robust computational design and evaluation of Peptide Vaccines for cellular immunity with application to sars cov 2
    bioRxiv, 2020
    Co-Authors: Ge Liu, Brandon Carter, Trenton Bricken, Siddhartha Jain, Mathias Viard, Mary Carrington, David K Gifford
    Abstract:

    We present a combinatorial machine learning method to evaluate and optimize Peptide Vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate Vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC Peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate Vaccines with multiple Peptide presentation algorithms to increase the probability that a Vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of Vaccine Peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate Peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based Vaccine platforms. Our SARS-CoV-2 MHC class I Vaccine formulations provide 93.21% predicted population coverage with at least five Vaccine Peptide-HLA hits on average in an individual (≥ 1 Peptide 99.91%) with all Vaccine Peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II Vaccine formulations provide 90.17% predicted coverage with at least five Vaccine Peptide-HLA hits on average in an individual with all Peptides having observed mutation probability ≤ 0.001. We evaluate 29 previously published Peptide Vaccine designs with our evaluation tool with the requirement of having at least five Vaccine Peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), Vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

Bertha Fru Asa - One of the best experts on this subject based on the ideXlab platform.

  • in silico design of a multi epitope Vaccine candidate against onchocerciasis and related filarial diseases
    Scientific Reports, 2019
    Co-Authors: Robert Adamu Shey, Stephen Mbigha Ghogomu, Kevin K Esoh, Neba Derrick Nebangwa, Cabirou Mounchili Shintouo, Nkemngo Francis Nongley, Bertha Fru Asa, Ferdinand Njume Ngale
    Abstract:

    Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic Vaccine targeting the infective L3 and microfilaria stages of the parasite’s life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a Vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit Vaccine Peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel Vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric Vaccine Peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative Peptide demonstrated antigenicity superior to current Vaccine candidates.

Nkemngo Francis Nongley - One of the best experts on this subject based on the ideXlab platform.

  • in silico design of a multi epitope Vaccine candidate against onchocerciasis and related filarial diseases
    Scientific Reports, 2019
    Co-Authors: Robert Adamu Shey, Stephen Mbigha Ghogomu, Kevin K Esoh, Neba Derrick Nebangwa, Cabirou Mounchili Shintouo, Nkemngo Francis Nongley, Bertha Fru Asa, Ferdinand Njume Ngale
    Abstract:

    Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic Vaccine targeting the infective L3 and microfilaria stages of the parasite’s life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a Vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit Vaccine Peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel Vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric Vaccine Peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative Peptide demonstrated antigenicity superior to current Vaccine candidates.