SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences. (December 2016)

• Record Type: Journal Article
• Authors: Jandrlić, Davorka R.
• Is Part Of: Computational biology and chemistry. Volume 65(2016)
• Page Start: 117
• Page End: 127
• Publication Date: 2016-12
• Subjects: T-cell epitope -- MHC I binding prediction -- Data mining -- Support vector machine -- Encoding scheme; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.10.011
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The new approach for peptide representation is proposed. Different models for quantitative and qualitative MHC binding prediction are developed. Accuracy of developed models is comparable or outperforms the best currently existing methods. Abstract: At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.