Learning MHC I—peptide binding
,*
1 Microsoft Research, Redmond WA 98052 USA
2 Dept. of Molecular Genetics and Biotechnology Hadassah Medical School, The Hebrew University of Jerusalem Israel
*To whom correspondence should be addressed.
Motivation and results: Motivated by the ability of a simple threading approach to predict MHC I—peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were used in the original threading approach, we included three other sources of information on peptide-MHC binding: (1) MHC class I sequences; (2) known binding energies for a large number of MHC-peptide complexes; and (3) an even larger binary dataset that contains information about strong binders (epitopes) and non-binders (peptides that have a low affinity for a particular MHC molecule). Our model significantly outperforms the standard threading approach in binding energy prediction. In our approach, which we call adaptive double threading, the parameters of the threading model are learnable, and both MHC and peptide sequences can be threaded onto structures of other alleles. These two properties make our model appropriate for predicting binding for alleles for which very little data (if any) is available beyond just their sequence, including prediction for alleles for which 3D structures are not available. The ability of our model to generalize beyond the MHC types for which training data is available also separates our approach from epitope prediction methods which treat MHC alleles as symbolic types, rather than biological sequences. We used the trained binding energy predictor to study viral infections in 246 HIV patients from the West Australian cohort, and over 1000 sequences in HIV clade B from Los Alamos National Laboratory database, capturing the course of HIV evolution over the last 20 years. Finally, we illustrate short-, medium-, and long-term adaptation of HIV to the human immune system.
Availability: http://www.research.microsoft.com/~jojic/hlaBinding.html
Contact: jojic{at}microsoft.com
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  H. Zhang, O. Lund, and M. Nielsen The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding Bioinformatics, May 15, 2009; 25(10): 1293 - 1299. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Zhang, C. Lundegaard, and M. Nielsen Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods Bioinformatics, January 1, 2009; 25(1): 83 - 89. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Jacob and J.-P. Vert Efficient peptide-MHC-I binding prediction for alleles with few known binders Bioinformatics, February 1, 2008; 24(3): 358 - 366. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Lundegaard, O. Lund, C. Kesmir, S. Brunak, and M. Nielsen Modeling the adaptive immune system: predictions and simulations Bioinformatics, December 15, 2007; 23(24): 3265 - 3275. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. C. Tong, T. W. Tan, and S. Ranganathan Methods and protocols for prediction of immunogenic epitopes Brief Bioinform, March 1, 2007; 8(2): 96 - 108. [Abstract] [Full Text] [PDF]
|
|