Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Menaka Rajapakse, Lonce Wyse, Bertil Schmidt, Vladimir Brusic

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A g7 molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-Ag7 have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all Ag7 data sets with high accuracy. A binding score matrix representing peptide binding motif to Ag7 was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported motifs.

Original languageEnglish
Pages (from-to)440-447
Number of pages8
JournalLecture Notes in Computer Science
Volume3578
Publication statusPublished - Oct 18 2005
Event6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 - Brisbane, Australia
Duration: Jul 6 2005Jul 8 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm'. Together they form a unique fingerprint.

Cite this