Predicting affinity and specificity of antigenic peptide binding to major histocompatibility class I molecules

Current Protein and Peptide Science

Volumn 10, Issue 3, 2009, Pages 286-296

  Sieker, Florian ^a   May, Andreas ^a   Zacharias, Martin ^a  

^a JACOBS UNIVERSITY BREMEN   (Germany)

Author keywords

Antigen presentation; Binding affinity prediction; Class I molecule; Ligand receptor docking; Machine learning; Neural network prediction

Indexed keywords

HLA ANTIGEN CLASS 1; MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 1; T LYMPHOCYTE RECEPTOR; EPITOPE; PEPTIDE;

ACCURACY; ADAPTIVE IMMUNITY; ALLELE; ALPHA HELIX; ANTIGEN BINDING; ARTIFICIAL NEURAL NETWORK; BETA SHEET; BINDING AFFINITY; BINDING SITE; BIOCHEMISTRY; BIOINFORMATICS; BIOPHYSICS; CANCER CELL; CELL SURFACE; CRYSTAL STRUCTURE; CYTOTOXICITY; ENDOPLASMIC RETICULUM; HIDDEN MARKOV MODEL; IMMUNE RESPONSE; MAMMAL; MOLECULAR DYNAMICS; MOLECULAR MODEL; PREDICTION; PROTEIN BINDING; PROTEIN DATABASE; PROTEIN MOTIF; PROTEIN STRUCTURE; PROTEIN TRANSPORT; QUALITY CONTROL; RECEIVER OPERATING CHARACTERISTIC; REVIEW; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; T LYMPHOCYTE; VIRUS IMMUNITY; CHEMISTRY; HUMAN; IMMUNOLOGY; PROBABILITY;

MAMMALIA;

EPITOPES; HISTOCOMPATIBILITY ANTIGENS CLASS I; HUMANS; MARKOV CHAINS; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEIN BINDING;

EID: 69449097120     PISSN: 13892037     EISSN: None     Source Type: Journal    
DOI: 10.2174/138920309788452191     Document Type: Review

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