Weighted lambda superstrings applied to vaccine design.
Identifieur interne : 000647 ( PubMed/Corpus ); précédent : 000646; suivant : 000648Weighted lambda superstrings applied to vaccine design.
Auteurs : Luis Martínez ; Martin Milani ; Iker Malaina ; Carmen Álvarez ; Martín-Blas Pérez ; Ildefonso M De La FuenteSource :
- PloS one [ 1932-6203 ] ; 2019.
English descriptors
- KwdEn :
- AIDS Vaccines (chemical synthesis), AIDS Vaccines (immunology), Algorithms, Epitopes (genetics), Epitopes (immunology), HIV-1 (genetics), HIV-1 (immunology), Humans, Immunoglobulin lambda-Chains (genetics), Immunoglobulin lambda-Chains (immunology), Models, Theoretical, Sequence Alignment, Vaccines (chemical synthesis), Vaccines (immunology), nef Gene Products, Human Immunodeficiency Virus (genetics), nef Gene Products, Human Immunodeficiency Virus (immunology).
- MESH :
- chemical , chemical synthesis : AIDS Vaccines, Vaccines.
- chemical , genetics : Epitopes, Immunoglobulin lambda-Chains, nef Gene Products, Human Immunodeficiency Virus.
- chemical , immunology : AIDS Vaccines, Epitopes, Immunoglobulin lambda-Chains, Vaccines, nef Gene Products, Human Immunodeficiency Virus.
- genetics : HIV-1.
- immunology : HIV-1.
- Algorithms, Humans, Models, Theoretical, Sequence Alignment.
Abstract
We generalize the notion of λ-superstrings, presented in a previous paper, to the notion of weighted λ-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted λ-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic λ-superstrings that at the same time present similar conformations to the Nef virus proteins.
DOI: 10.1371/journal.pone.0211714
PubMed: 30735507
Links to Exploration step
pubmed:30735507Le document en format XML
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sort="Alvarez, Carmen" uniqKey="Alvarez C" first="Carmen" last="Álvarez">Carmen Álvarez</name><affiliation><nlm:affiliation>IDIVAL Valdecilla Biomedical Research Institute, Santander, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Perez, Martin Blas" sort="Perez, Martin Blas" uniqKey="Perez M" first="Martín-Blas" last="Pérez">Martín-Blas Pérez</name><affiliation><nlm:affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="M De La Fuente, Ildefonso" sort="M De La Fuente, Ildefonso" uniqKey="M De La Fuente I" first="Ildefonso" last="M De La Fuente">Ildefonso M De La Fuente</name><affiliation><nlm:affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno 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Fuente</name><affiliation><nlm:affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>AIDS Vaccines (chemical synthesis)</term><term>AIDS Vaccines (immunology)</term><term>Algorithms</term><term>Epitopes (genetics)</term><term>Epitopes (immunology)</term><term>HIV-1 (genetics)</term><term>HIV-1 (immunology)</term><term>Humans</term><term>Immunoglobulin lambda-Chains (genetics)</term><term>Immunoglobulin lambda-Chains (immunology)</term><term>Models, Theoretical</term><term>Sequence Alignment</term><term>Vaccines (chemical synthesis)</term><term>Vaccines (immunology)</term><term>nef Gene Products, Human Immunodeficiency Virus 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Theoretical</term><term>Sequence Alignment</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We generalize the notion of λ-superstrings, presented in a previous paper, to the notion of weighted λ-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted λ-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic λ-superstrings that at the same time present similar conformations to the Nef virus proteins.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30735507</PMID><DateCompleted><Year>2019</Year><Month>11</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>09</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>2</Issue><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Weighted lambda superstrings applied to vaccine design.</ArticleTitle><Pagination><MedlinePgn>e0211714</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0211714</ELocationID><Abstract><AbstractText>We generalize the notion of λ-superstrings, presented in a previous paper, to the notion of weighted λ-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted λ-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic λ-superstrings that at the same time present similar conformations to the Nef virus proteins.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Martínez</LastName><ForeName>Luis</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0002-6297-1449</Identifier><AffiliationInfo><Affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Basque Center for Applied Mathematics BCAM, Bilbao, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Milanič</LastName><ForeName>Martin</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0002-8222-8097</Identifier><AffiliationInfo><Affiliation>University of Primorska, UP IAM and UP FAMNIT, Koper, Slovenia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Malaina</LastName><ForeName>Iker</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Álvarez</LastName><ForeName>Carmen</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>IDIVAL Valdecilla Biomedical Research Institute, Santander, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pérez</LastName><ForeName>Martín-Blas</ForeName><Initials>MB</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>M de la Fuente</LastName><ForeName>Ildefonso</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Nutrition, CEBAS-CSIC Institute, Murcia, Spain.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>02</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS 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