Prediction of MHC class I binding peptides, using SVMHC

By Pierre Dönnes, Arne Elofsson. 2002

344 citations recorded by the source corpus.

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.

Subfields: Immunology, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

In Molecular Biology

Citations in the corpus

Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins (2017, methodology): “Approaches have included sequence-based profiles [8] [9], structure-based predictions [10], generative probabilistic models [11], and machine learning [12] [13] [14].”
NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007, background): “One way to lower a potential bias in the negative data set is to add random data with assumed weak binding affinity values [18].”
Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles (2004, methodology): “Unfortunately, because there are very few experimentally verified examples of peptides that do not bind to a particular MHC, we have followed an approach similar to that of Donnes and Elofsson (2002) to obtain a set of non-binder peptides.”
PlasmoDB: the Plasmodium genome resource. A database integrating experimental and computational data (2003, methodology): “To assist in the identification of potential vaccine targets, annotated genes have been scored for potential T-cell epitopes using the SYFPEITHI method (8).”
Novel Machine Learning Methods for MHC Class I Binding Prediction (2010, methodology): “Many computational methods for the classification of peptides into MHC-I binders or non-binders have been proposed: ranging from matrices [14, 17] to machine learning [3, 1].”
An overview of bioinformatics tools for epitope prediction: Implications on vaccine development (2015, methodology): “SVMHC uses the matrices developed by the TEPITOPE software [47].”
NetMHCpan, a method for MHC class I binding prediction beyond humans (2008, neutral): “…allelespecific MHC class I binding prediction methods are limited to alleles that are characterized by peptide-binding data (Brusic et al. 1994; Buus et al. 2003; Donnes and Elofsson 2002; Lundegaard et al. 2008; Mamitsuka 1998; Nielsen et al. 2003; Segal et al. 2001; Tenzer et al. 2005).”
Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, neutral): “de/Services/SVMHC/ SVM I and II — — — — [95]”