Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network
- Author(s)
- Brusic, V; Rudy, G; Honeyman, M; Hammer, J; Harrison, L;
- Details
- Publication Year 1998,Volume 14,Issue #2,Page 121-130
- Journal Title
- BIOINFORMATICS
- Publication Type
- Journal Article
- Abstract
- Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.
- Publisher
- OXFORD UNIV PRESS
- Keywords
- MOLECULES; RESIDUES
- Publisher's Version
- https://doi.org/10.1093/bioinformatics/14.2.121
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 1998-01-01 12:00:00