Prediction of MHC class I binding peptides, using SVMHC

Pierre Dönnes,Arne Elofsson

doi:10.1186/1471-2105-3-25

Abstract

BackgroundT-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested.ResultsHere, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/.ConclusionsPrediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.

Highlights

T-cells are key players in regulating a specific immune response
Since all machine learning methods need a sufficient amount of data for training, we investigated the number of known binders needed for training, using three examples with a large set of known binders
If SYFPEITHI data was used prediction could only be done for 6 different Major Histocompatibility Complex (MHC) molecules

Summary

Introduction

T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. Sequences from pathogens provide a huge amount of potential vaccine candidates, as the activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules (for humans the term Human Leukocyte Antigens, HLA, is often used instead of MHC). The peptides that bind to HLAA*0201 are often 9 amino acids long (nonamers), and frequently have two anchor residues, a lysine in position 2 and a Valine in position 9 [3] This type of sequence patterns has been used as a simple prediction method [4]. SYFPEITHI prediction can be done for 13 different MHC class I types Another profile based MHC-peptide predictor is HLA_BIND at [http://bimas.dcrt.nih.gov/molbio/ hla_bind/]. It has been shown that profile based methods are correct in about 30% of the time, in the sense that one third of the predicted binders bind [8]

Methods

Results

Conclusion