Analytical Methods in Environmental Chemistry Journal
AMECJ

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

Document Type : Original Article

Authors

Mostafa Hassanisadi 1
Morteza G. Khaledi 2
Mehdi Jalali-Heravi 3

1 Nanotechnology Research Center, Research Institute of Petroleum Industry

2 Department of Chemistry, Sharif University of Technology

3 Department of Chemistry, North Carolina State University, NC27695-8204, USA

https://doi.org/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.

Graphical Abstract

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

Keywords

Micellar ElectroKinetic Chromatography
Artificial neural networks
Adaptive neuro-fuzzy inference system
Peptide mobilities
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Analytical Methods in Environmental Chemistry Journal
Volume 3, Issue 2
Spring 2020
Pages 5-20