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

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 score (Q2) estimated 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 Rpre2 = 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.

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.

Modified Deacetylcephalosporin C Synthase for the Biotransformation of Semisynthetic Cephalosporins.

UNLABELLED: Deacetylcephalosporin C synthase (DACS), a 2-oxoglutarate-dependent oxygenase synthesized by Streptomyces clavuligerus, transforms an inert methyl group of deacetoxycephalosporin C (DAOC) into an active hydroxyl group of deacetylcephalosporin C (DAC) during the biosynthesis of cephalosporin. It is a step which is chemically difficult to accomplish, but its development by use of an enzymatic method with DACS can facilitate a cost-effective technology for the manufacture of semisynthetic cephalosporin intermediates such as 7-amino-cephalosporanic acid (7ACA) and hydroxymethyl-7-amino-cephalosporanic acid (HACA) from cephalosporin G. As the native enzyme showed negligible activity toward cephalosporin G, an unnatural and less expensive substrate analogue, directed-evolution strategies such as random, semirational, rational, and computational methods were used for systematic engineering of DACS for improved activity. In comparison to the native enzyme, several variants with improved catalytic efficiency were found. The enzyme was stable for several days and is expressed in soluble form at high levels with significantly higher kcat/Km values. The efficacy and industrial scalability of one of the selected variants, CefFGOS, were demonstrated in a process showing complete bioconversion of 18 g/liter of cephalosporin G into deacetylcephalosporin G (DAG) in about 80 min and showed reproducible results at higher substrate concentrations as well. DAG could be converted completely into HACA in about 30 min by a subsequent reaction, thus facilitating scalability toward commercialization. The experimental findings with several mutants were also used to rationalize the functional conformation deduced from homology modeling, and this led to the disclosure of critical regions involved in the catalysis of DACS. IMPORTANCE: 7ACA and HACA serve as core intermediates for the manufacture of several semisynthetic cephalosporins. As they are expensive, a cost-effective enzyme technology for the manufacture of these intermediates is required. Deacetylcephalosporin C synthase (DACS) was identified as a candidate enzyme for the development of technology from cephalosporin G in this study. Directed-evolution strategies were employed to enhance the catalytic efficiency of deacetylcephalosporin C synthase. One of the selected mutants of deacetylcephalosporin C synthase could convert high concentrations of cephalosporin G into DAG, which subsequently could be converted into HACA completely. As cephalosporin G is inexpensive and readily available, the technology would lead to a substantial reduction in the cost for these intermediates upon commercialization.