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Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis

Jalali Heravi, M ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1016/j.chroma.2005.09.043
  3. Publisher: 2005
  4. Abstract:
  5. 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 (E s,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
  6. Keywords:
  7. Amino acids ; Backpropagation ; Electrophoresis ; Enzymes ; Linear systems ; Mathematical models ; Neural networks ; Regression analysis ; Capillary zone electrophoresis ; Electrophoretic mobility ; Peptide mobility ; Proteins ; Cytochrome c ; Melittin ; Proteinase ; Amino acid sequence ; Artificial neural network ; Hydrophobicity ; Linear regression analysis ; Molecular size ; Priority journal ; Protein transport ; Stereospecificity ; Cytochromes c ; Electrophoresis, Capillary ; Glucagon ; Molecular Sequence Data ; Multivariate Analysis ; Neural Networks (Computer) ; Oligopeptides ; Peptide Fragments ; Peptide Mapping ; Equus caballus
  8. Source: Journal of Chromatography A ; Volume 1096, Issue 1-2 , 2005 , Pages 58-68 ; 00219673 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0021967305018212?via%3Dihub