Quantitative Prediction of Class I MHC/Epitope Binding Affinity using QSAR Modeling Derived from Amino Acid Structural Information.

In this study, a comprehensive quantitative prediction method of binding affinity was established using quantitative structure-activity relationship (QSAR) modeling derived from amino acid physicochemical information. Firstly, the epitope was characterized by a set of amino acid physicochemical parameters. Secondly, the structural variables were optimized by the stepwise regression (STR). Finally, the robust quantitative models with were built by multiple linear regressions (MLR) for 31 MHC class I subtypes. The normalized regression coefficients (NRCs) of QSAR model could demonstrate the mechanism of interaction of MHC, epitope, and TCR very well. The contribution of amino acid at each position of epitope, which was calculated by NRC, could determine which one was favorable for binding affinity or not. Therefore, the quantitative models established by STR-MLR could be used to guide virtual combinational design and high throughput screening of CTL epitope. Besides, they have many advantages, such as definite physiochemical indication, easier calculation and explanation, and good performances.
PMID: 25612766 [PubMed - as supplied by publisher]

Source: Combinatorial Chemistry and High Throughput Screening - January 21, 2015 Category: Chemistry Authors: Wang Y, Zhou P, Lin Y, Shu M, Hu Y, Xia Q, Lin Z Tags: Comb Chem High Throughput Screen Source Type: research