Mostafa Hassanisadi - Nanotechnology Research Center, Research Institute of Petroleum Industry, Tehran, Iran
Morteza G.Khaledi - Department of Chemistry, Sharif University of Technology, P.O.Box۹۵۱۶-۱۱۳۶۵, Tehran, Iran
Mehdi Jalali-Heravi - Department of Chemistry, North Carolina State University, NC۸۲۰۴-۲۷۶۹۵, USA

Mobility of ۱۲۸ peptides composed of up to ۱۴ amino acids is determined for sodium dodecyl sulfate (SDS) micellar systems using micellar electrokinetic chromatography (MEKC). The mobilities of these peptides are predicted using back propagation of error artificial neural networks (BP-ANNs). Adaptive neuro-fuzzy inference system (ANFIS) which can deal with linear and nonlinear phenomena is used to select the inputs of BP-ANN. A ۳:۴:۱ BP-ANN model with four variables of Kappa substituent constant, Kappa(H), number of peptide bonds, (lnN), molar refractivity of C-terminal, MRC, and steric effects at N-terminal, ES,N, which incorporate substituent, steric and molar refractivity effects as its inputs was developed. Comparison of Multiple Linear Regression (MLR) and ANN results shows the nonlinear characteristic of the phenomena. The nonlinear model was successful in predicting the mobilities of ۱۲۰ peptides except for the ones (۸ peptides) with negatively charged amino acids. It is shown that that most outlier peptides contain middle glutamic acid (E) and aspartic acid (D) amino acids and their mobilities follow a similar mechanism in MEKC.

Peptide mobilities, Micellar ElectroKinetic Chromatography, Artificial neural networks, Adaptive neuro-fuzzy inference system