Authors:
Shib Sankar Bhowmick
1
;
Indrajit Saha
2
;
Giovanni Mazzocco
3
;
Ujjwal Maulik
2
;
Luis Rato
4
;
Debotosh Bhattacharjee
2
and
Dariusz Plewczynski
3
Affiliations:
1
Jadavpur University and University of Evora, India
;
2
Jadavpur University, India
;
3
University of Warsaw, Poland
;
4
University of Evora, Portugal
Keyword(s):
HLA Class II, Machine Learning, MHC, Peptide Binding, T Cell Epitopes.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Algorithms and Software Tools
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Computational Intelligence
;
Data Mining and Machine Learning
;
Enterprise Information Systems
;
Immuno- and Chemo-Informatics
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Operational Research
;
Pattern Recognition, Clustering and Classification
;
Simulation
;
Soft Computing
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 t
hese 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.
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