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In the present study, a machine learning (ML) model was developed to predict the epistatic phenomena of combination mutants to improve the anticancer antibody-drug trastuzumab's binding affinity towards its antigen human epidermal growth factor receptor 2 (HER2). An ML algorithm, Support Vector Regression (SVR) was used to develop ML models with a data set consists of 193 affinity values of single mutants of trastuzumab and its associated various amino acid sequence derived descriptors. The subset selection of descriptors and SVR hyperparameters were done using the Genetic Algorithm (GA) within the SVR and the wrapper approach called GA-SVR. A 100 evolutionary cycles of GA produced the best 100 probable GA-SVR models based on their fitness scoreestimated using a stratified 5 fold cross-validation procedure. The final ML model found to be highly predictive of test data set of six combination mutants and one single mutant with= 0.71. The analysis of descriptors in the ML model highlighted the importance of mutant induced secondary structural variation causes the binding affinity variation of the trastuzumab. The same was verified using a short 20 ns and a long 100 ns in duplicate molecular dynamics simulation of a wild and mutant variant of trastuzumab. The secondary structure induced affinity change due to mutations in the CDR-H3 is a novel insight that came out of this study. That should help rational mutant selection to develop a biobetter trastuzumab with a multifold improved binding affinity into the market quickly. Communicated by Ramaswamy H. Sarma

journal of biomolecular structure and dynamics

Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2