Mobility modeling of peptides in capillary electrophoresis

摘要: Abstract Recent rapid developments in proteomics require high-resolution separation of a large number peptides for their downstream identification by mass spectrometry. Capillary electrophoresis (CE) is an electric-field-mediated bioanalytical technique capable rapid, very complex sample mixtures. Development CE methods adequate usually time-consuming task. Application model-based approaches to predict peptide mobilities from known physicochemical properties can shorten tedious experimental optimization separation. This endeavor requires specification structural descriptors followed selection appropriate modeling methods. To date, numerous theoretical predictive models have been developed, mostly based on Stokes’ Law relate (e.g., charge and size). However, these two-variable could not successfully electrophoretic all categories with reasonable degree accuracy. address the shortcomings models, new strategies were recently introduced, including usage additional or applying non-linear artificial neural networks), attain more accurate, robust prediction. Effective application machine-learning techniques development has consolidated conjecture relationships between mobilities. In this article, we review recent advances mobility peptides, particularly respect predicting optimal conditions analysis highly mixtures applications.