Current Computer-Aided Drug Design

Subhash C. Basak
Departments of Chemistry, Biochemistry & Molecular Biology University of Minnesota Duluth
Duluth, MN 55811
USA

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TAP-Binding Peptides Prediction by QSAR Modeling Based on Amino Acid Structural Information

Author(s): Yuanqing Wang, Xiaoming Cheng, Yong Lin, Haixia Wen, Li Wang, Qingyou Xia, Zhihua Lin.

Abstract:

The transporter associated with antigen processing (TAP) is essential for peptide delivery from the cytosol into the lumen of the endoplasmic reticulum (ER), where these peptides are loaded on a major histocompatibility complex (MHC) I molecules and form peptide-MHC complex. The peptide-MHC leaves the ER and displays their antigenic cargo on the cell surface to cytotoxic T cells. In this study, 89 physicochemical properties of amino acid were collected from AAIndex database, and used to characterize the peptides which were binding to TAP. Then, the stepwise regression (STR) was used to optimize the parameters which characterized the TAP binding peptides, and the multiple linear regression (MLR) was used to construct the quantitative structural activity relationship (QSAR) model based on optimized parameters. The quantitative models had good reliability and predictive ability: the Q2 of “leave one out” validation is 0.676 and R2 of test dataset is 0.722 respectively. Additionally, the standardized coefficients of the models could demonstrate the attributions for each position of epitope and determine which special amino acid is suitable at any position of the peptide. Therefore, the QSAR model constructed by STR-MLR has many advantages, such as, easier calculation and explanation, good performance, and definite physiochemical indication, which could be used to guide the design and modification of the TAP binding peptide.

Keywords: Transporter associated with antigen processing (TAP), major histocompatibility complex (MHC), TAP binding peptide, structural descriptors, stepwise regression (STR), multiple linear regression (MLR), quantitative structural activity relationship (QSAR), structural characterization, artificial neural networks, drug design

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Article Details

VOLUME: 8
ISSUE: 1
Year: 2012
Page: [50 - 54]
Pages: 5
DOI: 10.2174/157340912799218499