Viral peptides-MHC interaction: Binding probability and distance from human peptides.
Identifieur interne : 000721 ( PubMed/Checkpoint ); précédent : 000720; suivant : 000722Viral peptides-MHC interaction: Binding probability and distance from human peptides.
Auteurs : Daniele Santoni [Italie]Source :
- Journal of immunological methods [ 1872-7905 ] ; 2018.
Descripteurs français
- KwdFr :
- Antigènes d'histocompatibilité de classe I (immunologie), Déterminants antigéniques des lymphocytes T (immunologie), Génome viral, Herpèsvirus humain de type 1 (immunologie), Humains, Liaison aux protéines, Oligopeptides (immunologie), Probabilité, Protéines virales (immunologie), VIH-1 (Virus de l'Immunodéficience Humaine de type 1) (immunologie).
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , immunology : Epitopes, T-Lymphocyte, Histocompatibility Antigens Class I, Oligopeptides, Viral Proteins.
- immunology : HIV-1, Herpesvirus 1, Human.
- Genome, Viral, Humans, Probability, Protein Binding.
Abstract
Identification of peptides binding to MHC class I complex can play a crucial role in retrieving potential targets able to trigger an immune response. Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.
DOI: 10.1016/j.jim.2018.05.009
PubMed: 29800577
Affiliations:
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Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29800577</PMID><DateCompleted><Year>2019</Year><Month>09</Month><Day>12</Day></DateCompleted><DateRevised><Year>2019</Year><Month>09</Month><Day>12</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1872-7905</ISSN><JournalIssue CitedMedium="Internet"><Volume>459</Volume><PubDate><Year>2018</Year><Month>08</Month></PubDate></JournalIssue><Title>Journal of immunological methods</Title><ISOAbbreviation>J. Immunol. Methods</ISOAbbreviation></Journal><ArticleTitle>Viral peptides-MHC interaction: Binding probability and distance from human peptides.</ArticleTitle><Pagination><MedlinePgn>35-43</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0022-1759(17)30494-5</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jim.2018.05.009</ELocationID><Abstract><AbstractText>Identification of peptides binding to MHC class I complex can play a crucial role in retrieving potential targets able to trigger an immune response. Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.</AbstractText><CopyrightInformation>Copyright © 2018 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Santoni</LastName><ForeName>Daniele</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Institute for System Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, Via dei Taurini 19, Rome 00185, Italy. 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