About: Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

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About: Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms 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	Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms
Creator	González-Díaz, Humberto Pérez-Montoto, Lázaro Ubeira, Florencio
source	Medline; PMC
abstract	Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.
has issue date	2014-01-12(xsd:dateTime)
bibo:doi	10.1155/2014/768515
bibo:pmid	24741624
has license	cc-by
sha1sum (hex)	a0f4a467be80972f434dabf027d2b5260b608740
schema:url	https://doi.org/10.1155/2014/768515
resource representing a document's title	Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms
has PubMed Central identifier	PMC3987976
has PubMed identifier	24741624
schema:publication	J Immunol Res
resource representing a document's body	covid:a0f4a467be80972f434dabf027d2b5260b608740#body_text
is schema:about of	named entity 'ORGANISMS' named entity 'HOST' named entity 'ORGANISMS' covid:arg/a0f4a467be80972f434dabf027d2b5260b608740 named entity 'VACCINE DESIGN' named entity 'RECENT' named entity 'EXACT SOLUTION' named entity 'APPROACH' named entity 'IN VIVO' named entity 'METHODS' named entity 'ACCURACY' named entity 'GENERAL' named entity 'SOLUTION' named entity 'PEPTIDE SEQUENCE' named entity 'VALIDATION' named entity 'THEORY' named entity 'TERMS' named entity 'NEW' named entity 'OUR' named entity 'EXPERIMENTAL ASSAY' named entity 'RELATIONSHIP' named entity 'BOUNDARY ' named entity 'KNOWN' named entity 'CASES' named entity 'COMPUTATIONAL' named entity 'EPITOPES' named entity 'STRUCTURAL' named entity 'HOST' named entity 'SPECIFICITY' named entity 'MODEL' named entity 'GIVEN' named entity 'SOURCE' named entity 'PROCESS' named entity 'CLASSIFY' named entity 'WORK' named entity 'ORGANISM' named entity 'QUANTITATIVE' named entity 'POSSIBLE' named entity 'KNOWLEDGE' named entity 'MORE THAN' named entity 'EPITOPE' named entity 'BIOLOGICAL PROCESS' named entity 'PEPTIDE' named entity 'VACCINE DESIGN' named entity 'STUDY' named entity 'PURPOSE' named entity 'INTRODUCED' named entity 'INCLUDE' named entity 'EXPERIMENTAL' named entity 'MODEL' named entity 'REPORTS' named entity 'INVOLVING' named entity 'COMPLEX' named entity 'BIOMOLECULAR SYSTEMS' named entity 'PEPTIDES' named entity 'DETERMINED' named entity 'CHANGES' named entity 'EXPERIMENTAL TECHNIQUES' named entity 'PROBLEM' named entity 'STRUCTURE' named entity 'SENSITIVITY' named entity 'PERTURBATION' named entity 'USEFUL' named entity 'VARIATION' named entity 'CONDITIONS' named entity 'SOURCE' named entity 'PROPERTY' named entity 'ADD' named entity 'OPTIMIZATION' named entity '200' named entity 'TRAINING' named entity 'EPITOPES' named entity 'SERIES' named entity 'TYPE' named entity 'ACTION' named entity 'EXPERIMENTAL TECHNIQUES' named entity 'IMMUNOLOGY' named entity 'RELATED' named entity 'PREDICTION'

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