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Title: Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
Authors: Bhowmick, ShibSankar
Saha, Indrajit
Mazzocco, Giovanni
Maulik, Ujjwal
Rato, Luis
Bhattacharjee, Debotosh
Plewczynski, Dariusz
Editors: Pastor, Oscar
Sinoquet, Christine
Plantier, Guy
Schultz, Tanja
Fred, Ana
Gamboa, Hugo
Keywords: HLA Class II
Machine Learning
Peptide Binding
T Cell Epitopes
Issue Date: Mar-2014
Publisher: Science and Technology Publications
Citation: Bhowmick S., Saha I., Mazzocco G., Maulik U., Rato L., Bhattacharjee D. and Plewczynski D. (2014). Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014), ISBN 978-989-758-012-3, pages 178-185. DOI: 10.5220/0004804801780185
Abstract: In this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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