title: Prediction of binding to MHC class I molecules.
abstract: The binding of antigenic peptide sequences to major histocompatibility complex (MHC) molecules is a prerequisite for stimulation of cytotoxic T cell responses. Neural networks are here used to predict the binding capacity of polypeptides to MHC class I molecules encoded by the gene HLA-A*0201. Given a large database of 552 nonamers and 486 decamers and their known binding capacities, the neural networks achieve a predictive hit rate of 0.78 for classifying peptides which might induce an immune response (good or intermediate binders) vs. those which cannot (weak or non-binders). The neural nets also depict specific motifs for different binding capacities. This approach is in principle applicable to all MHC class I and II molecules, given a suitable set of known binding capacities. The trained networks can then be used to perform a systematic search through all pathogen or tumor antigen protein sequences for potential cytotoxic T lymphocyte epitopes.
authors.affiliation: Department of Molecular and Experimental Medicine, Scripps Research Institute, La Jolla, CA 92037, USA.
authors.forename: H P
authors.initials: HP
authors.lastname: Adams
authors.forename: J A
authors.initials: JA
authors.lastname: Koziol
mesh_terms: D015234:HLA-A Antigens / Q000378:metabolism*; D066298:In Vitro Techniques; D016571:Neural Networks, Computer; D010455:Peptides / Q000276:immunology* / Q000378:metabolism; D011485:Protein Binding; D013329:Structure-Activity Relationship
chemical_list: D015234:HLA-A Antigens; D010455:Peptides