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Open Access Research article

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

Bernhard Knapp1*, Verena Giczi12, Reiner Ribarics1 and Wolfgang Schreiner1

Author Affiliations

1 Center for Medical Statistics, Informatics and Intelligent Systems, Department for Biosimulation and Bioinformatics, Medical University of Vienna, Vienna, Austria

2 University of Applied Sciences, FH Campus Wien, Department of Bioengineering, Vienna, Austria

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BMC Bioinformatics 2011, 12:241  doi:10.1186/1471-2105-12-241

Published: 17 June 2011

Abstract

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.