Determination and prediction of peptide mobilities by micellar electro-kinetic chromatography using adaptive neuro-fuzzy inference system as a feature selection method

Morteza G Khaledi,Mehdi Jalali-Heravi,Mustafa Sadi

doi:10.24200/amecj.v3.i02.98

Abstract

Mobility of 128 peptides composed of up to 14 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 3:4:1 BP-ANN model with four variables of Kappa substituent constant, Kappa(H), number of peptide bonds, (ln(N), 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 120 peptides except for the ones (8 peptides) with negatively charged amino acids.

Highlights

Micellar electrokinetic chromatography (MEKC) is a widely used technique in capillary electrophoresis (CE) and is capable of separating neutral compounds as well as charged solutes by including a pseudostationary phase [1,2,3,4,5,6]
We have chosen Adaptive neuro-fuzzy inference system (ANFIS) because of its much faster convergence, much more repeatability and much less preprocessing compared with artificial neural network (ANN). (3) In order to develop a model for predicting the micellar electrokinetic chromatography (MEKC) mobilities of peptides and inspecting the linear/nonlinear characteristics of the migration behavior of peptides in MEKC, simple Multiple Linear Regression (MLR) as a linear method and back propagation of error artificial neural networks (BP-ANNs) as a nonlinear method are used
We hope that the results of this work together with our previous works on CZE could pave the way for further studies on the 2D MEKC-CZE simulations

Summary

Introduction

Micellar electrokinetic chromatography (MEKC) is a widely used technique in capillary electrophoresis (CE) and is capable of separating neutral compounds as well as charged solutes by including a pseudostationary phase [1,2,3,4,5,6]. A long-range goal of our laboratory is developing experimental and theoretical methods for peptide separations, and mapping two-dimensional MEKC-CZE schemes. Reaching this goal requires an in-depth understanding of the effects of different factors on the CZE and MEKC peptide mobilities. The robustness of this work was shown by artificial neural network (ANN) modeling of the mobilities of 102 larger peptides – up to 42 amino acid residues – that included highly charged and hydrophobic peptides [10]. In the present work we have chosen adaptive neurofuzzy inference system (ANFIS) for selecting the most effective parameters on MEKC mobilities This method is capable in dealing with linear and nonlinear phenomena. Success in modeling of the electrophoretic mobilities of peptides using MEKC, together with our previous achievements in modeling of CZE mobilities might pave the way for developing and predicting the two-dimensional MEKC-CZE maps of peptides

Objectives

Methods

Results

Conclusion