About: Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

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About: Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2
Creator	Liu, Ge Viard, Mathias Carrington, Mary Bricken, Trenton Carter, Brandon Gifford, David Jain, Siddhartha
source	BioRxiv; Medline
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.
has issue date	2020-05-17(xsd:dateTime)
bibo:doi	10.1101/2020.05.16.088989
bibo:pmid	32511351
has license	biorxiv
sha1sum (hex)	3438e9ea06fef6e6e8e6d77b679e2112db2c955c
schema:url	https://doi.org/10.1101/2020.05.16.088989
resource representing a document's title	Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2
has PubMed identifier	32511351
schema:publication	bioRxiv
resource representing a document's body	covid:3438e9ea06fef6e6e8e6d77b679e2112db2c955c#body_text
is schema:about of	named entity 'evaluation' named entity 'peptide' named entity 'MHC class I' named entity 'vaccine' named entity 'evaluation' named entity 'haplotype' named entity 'objective function' named entity 'minimize' named entity 'design' named entity 'application' named entity 'IMPLEMENTATION' named entity 'SIGNAL SEQUENCE' named entity 'EPITOPE' named entity 'SUPERIOR' named entity 'DATA' named entity 'data' named entity 'Our' named entity 'optimization' named entity 'tool' named entity 'SARS-CoV-2' named entity 'MHC class II' named entity 'observed' named entity 'epitope' named entity 'peptides' named entity 'Our' named entity 'conditioned' named entity 'based' named entity 'haplotype' named entity 'peptide' named entity 'peptide' named entity 'candidate' named entity 'HLA' named entity 'vaccines' named entity 'machine learning' named entity 'epitopes' named entity 'HLA' named entity 'HLA' named entity 'MHC' named entity 'SARS-CoV-2' named entity 'MHC' named entity 'HLA' named entity 'MHC class II' named entity 'MHC' named entity 'haplotype' named entity 'peptide' named entity 'peptide vaccine' named entity 'clinical development' named entity 'SARS-CoV-2' named entity 'genomes' named entity 'peptide' named entity 'peptide' named entity 'peptide' named entity 'vaccines' named entity 'peptides' named entity 'peptide' named entity 'Asian' named entity 'peptides' named entity 'carbohydrates' named entity 'probability' named entity 'beam search' named entity 'peptides' named entity 'peptides' named entity 'beam search' named entity 'binding affinity' named entity 'immunogenic' named entity 'false positive' named entity 'binding affinity' named entity 'peptide' named entity 'peptides' named entity 'M A' named entity 'alleles' named entity 'peptides' named entity 'sampling' named entity 'probability' named entity 'peptides' named entity 'peptide sequences'

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