Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ1-minimization

BioData Mining

Clemente Aguilar-Bonavides¹  Reinaldo Sanchez-Arias²  Cristina Lanzas³ 

[1] National Institute for Mathematical and Biological Synthesis, University of Tennessee, 37996-3410 Knoxville, TN, USA

[2] Department of Applied Mathematics, Wentworth Institute of Technology, 02115 Boston, MA, USA

[3] Department of Biomedical and Diagnostic Sciences, University of Tennessee, 37996-3410 Knoxville, TN, USA

关键词: Classification algorithms;    Machine learning;    Immunoinformatics;    Epitope prediction;    MHC class II;    Sparse representation;    Peptide binding;   

Others  :  1083992
DOI  :  10.1186/1756-0381-7-23

 received in 2014-03-03, accepted in 2014-10-25,  发布年份 2014

【 摘 要 】

Background

The major histocompatibility complex (MHC) is responsible for presenting antigens (epitopes) on the surface of antigen-presenting cells (APCs). When pathogen-derived epitopes are presented by MHC class II on an APC surface, T cells may be able to trigger an specific immune response. Prediction of MHC-II epitopes is particularly challenging because the open binding cleft of the MHC-II molecule allows epitopes to bind beyond the peptide binding groove; therefore, the molecule is capable of accommodating peptides of variable length. Among the methods proposed to predict MHC-II epitopes, artificial neural networks (ANNs) and support vector machines (SVMs) are the most effective methods. We propose a novel classification algorithm to predict MHC-II called sparse representation via ℓ₁-minimization.

Results

We obtained a collection of experimentally confirmed MHC-II epitopes from the Immune Epitope Database and Analysis Resource (IEDB) and applied our ℓ₁-minimization algorithm. To benchmark the performance of our proposed algorithm, we compared our predictions against a SVM classifier. We measured sensitivity, specificity abd accuracy; then we used Receiver Operating Characteristic (ROC) analysis to evaluate the performance of our method. The prediction performance of MHC-II epitopes of the ℓ₁-minimization algorithm was generally comparable and, in some cases, superior to the standard SVM classification method and overcame the lack of robustness of other methods with respect to outliers. While our method consistently favoured DPPS encoding with the alleles tested, SVM showed a slightly better accuracy when “11-factor” encoding was used.

Conclusions

ℓ₁-minimization has similar accuracy than SVM, and has additional advantages, such as overcoming the lack of robustness with respect to outliers. With ℓ₁-minimization no model selection dependency is involved.

【 授权许可】

   
2014 Aguilar-Bonavides et al.; licensee BioMed Central Ltd.

【 图 表 】

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