Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment

Tissue Antigens

Volumn 83, Issue 2, 2014, Pages 94-100

  Carrasco Pro, S ^a   Zimic, M ^a   Nielsen, M ^b,c  

^a UNIVERSIDAD PERUANA CAYETANO HEREDIA   (Peru)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^c INSTITUTO DE INVESTIGACIONES BIOTECNOLÓGICAS   (Argentina)

Author keywords

Artificial Neural Networks; Binding specificity; CTL epitopes; Epitope prediction; MHC class I; Non human primates

Indexed keywords

LIGAND; MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 1;

ALLELE; AMINO ACID SEQUENCE; ANTIGEN BINDING; ANTIGEN STRUCTURE; ARTICLE; CATTLE; CHIMPANZEE; CONTROLLED STUDY; ENVIRONMENTAL FACTOR; GORILLA; HUMAN; LEUKOCYTE; MACACA; MAJOR HISTOCOMPATIBILITY COMPLEX; MOUSE; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; SWINE; VALIDATION PROCESS;

ARTIFICIAL NEURAL NETWORKS; BINDING SPECIFICITY; CTL EPITOPES; EPITOPE PREDICTION; MHC CLASS I; NON-HUMAN PRIMATES;

ALLELES; ANIMALS; BINDING SITES; CATTLE; DATABASES, PROTEIN; GORILLA GORILLA; HISTOCOMPATIBILITY ANTIGENS CLASS I; HUMANS; MACACA; MICE; MOLECULAR DOCKING SIMULATION; PAN TROGLODYTES; PEPTIDES; PROTEIN BINDING; PROTEIN INTERACTION DOMAINS AND MOTIFS; SOFTWARE; SWINE;

EID: 84892992809     PISSN: 00012815     EISSN: 13990039     Source Type: Journal    
DOI: 10.1111/tan.12292     Document Type: Article

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