@article{jalali-heravi_shen_hassanisadi_khaledi_2005, title={Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis}, volume={1096}, ISSN={["1873-3778"]}, DOI={10.1016/j.chroma.2005.09.043}, abstractNote={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 Offord's 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.}, number={1-2}, journal={JOURNAL OF CHROMATOGRAPHY A}, author={Jalali-Heravi, M and Shen, Y and Hassanisadi, M and Khaledi, MG}, year={2005}, month={Nov}, pages={58–68} }
 @article{jalali-heravi_shen_hassanisadi_khaledi_2005, title={Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks}, volume={26}, ISSN={["1522-2683"]}, DOI={10.1002/elps.200410308}, abstractNote={The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., µ ≡ Q/M2/3) gave the best correlation for the data set. A quantitative structure‐mobility relationship (QSMR) was developed using the Offord's charge‐over‐mass term (Q/M2/3) as one descriptor combined with the corrected steric substituent constant (Es, c) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3–4–1 back propagation artificial neural networks (BP‐ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP‐ANN analysis suggest the existence of nonlinear characteristic in the mobility‐charge relationships.}, number={10}, journal={ELECTROPHORESIS}, author={Jalali-Heravi, M and Shen, Y and Hassanisadi, M and Khaledi, MG}, year={2005}, month={May}, pages={1874–1885} }