Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Andrew J. Bordner, Hans D. Mittelmann

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Background: The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.Results: We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.Conclusions: The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

Original languageEnglish (US)
Article number41
JournalBMC Bioinformatics
Volume11
DOIs
StatePublished - Jan 20 2010

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Thermodynamics
Peptides
Affine transformation
Rapid thermal annealing
Prediction
Model
Class
Regularization
High Accuracy
Epitopes
Peptide Fragments
Predict
Vaccines
Adaptive Immunity
Overfitting
Vaccine
Immune Response
Binding Energy
Computational methods
Web Server

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model. / Bordner, Andrew J.; Mittelmann, Hans D.

In: BMC Bioinformatics, Vol. 11, 41, 20.01.2010.

Research output: Contribution to journalArticle

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