Application of proteometric approach for identification of functional mutant sites to improve the binding affinity of anticancer biologic trastuzumab with its antigen human epidermal growth factor receptor 2.

The aim of the present study was to develop a linear regression model aiding to a quick scan of the most important sites for mutation of an anticancer biologic trastuzumab. The important sites identified on trastuzumab can be used to carry out site-directed mutagenesis to improve the binding affinity of the drug towards its antigen, human epidermal growth factor receptor 2 (HER2). This will lead to low dosage requirement of the drug for treating cancer patients, which in turn help to cut the cost and combat development of resistance. A quantitative structure-activity relationship (QSAR) model was built by multiple linear regressions using genetic algorithm-based feature selection (GA-MLR) method using 48 dependent variables (dissociation constant Kd ) and 226 independent variables (theoretical descriptors generated using a proteometrics approach). The final QSAR model selected in the study was more on the basis of ability to predict accurately independent test data and generalization ability of the model rather than mere statistical significance of the model. With combined analysis of descriptors presented in final QSAR model and most frequent descriptors pooled from all solution models, it was demonstrated that the modeling procedure was able to bring on the factors important for antigen-antibody interactions with an example of HER2-trastuzumab interaction reported in previous experimental studies. This paper will allow the prediction of the most preferable site to mutate for improving the binding affinity of trastuzumab with HER2 and also will be helpful in selecting most preferable amino acids to substitute in the selected site for mutations. This is the novel report on proteometrics approach with autocorrelation formalism for antibody engineering, which can be extended to other antibody-antigen pairs.