Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offordʹs charge over mass term (Q/M2/3), corrected steric substituent constant (Es,c) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report – up to 42 amino acid residues as compared to 13 amino acids in the initial study – that also included highly charged and hydrophobic peptides. The entire data set was obtained from the published result by Janini and co-workers. The results of this model are compared with those obtained using multiple linear regressions (MLR) model developed in this work and the multi-variable model released by Janini et al. Better predictive ability of the BP-ANN model over the MLR indicates the non-linear characteristics of the electrophoretic mobility of peptides. The present model exhibits better robustness than the MLR models in predicting CZE mobilities of a diverse data set at different experimental conditions. To explore the utility of the ANN model in simulation of the CZE peptide maps, the profiles for the endoproteinase digests of melittin, glucagon and horse cytochrome C is studied in the present work.