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

Vol 3, Issue 02, Pages 5-20,*** Field: Chemometrics in Analytical Chemistry

  • Mostafa Hassanisadi (Corresponding Author)* Nanotechnology Research Center, Research Institute of Petroleum Industry
  • Morteza G. Khaledi Department of Chemistry, Sharif University of Technology
  • Mehdi Jalali-Heravi Department of Chemistry, North Carolina State University, NC27695-8204, USA
Keywords: Micellar ElectroKinetic Chromatography, Artificial neural networks, Adaptive neuro-fuzzy inference system, Peptide mobilities


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.


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How to Cite
Hassanisadi (Corresponding Author)*, M., G. Khaledi, M., & Jalali-Heravi, M. (2020). Determination and prediction of peptide mobilities by micellar electro-kinetic chromatography using adaptive neuro-fuzzy inference system as a feature selection method. Analytical Methods in Environmental Chemistry Journal, 3(02), 5-20.
Original Article