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Bioinformatics. 1998;14(2):121-30.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network.

Author information

1
The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.

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 binders 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 immunotherapeutic peptides.

AVAILABILITY:

Software and data are available from the authors upon request.

CONTACT:

vladimir@wehi.edu. au

[Indexed for MEDLINE]

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