Research Abstracts Online
January - December 2011
University of Minnesota Twin Cities
College of Food, Agricultural, and Natural Resource Sciences
of Horticultural Science
PI: Paul G. Boswell
Accurate HPLC Retention Prediction of Peptides in Gradient Elution by Back-Calculation of Gradient and Flow Rate Profiles
Use of HPCL retention information in combination with mass spectral information considerably improves the quantity of peptide identifications and confidence in them. Unfortunately, current models to predict peptide retention based on amino acid sequence are not very accurate. This is, in part, because they are all based in gradient elution, where the retention behavior is extremely complex because it is strongly influenced (a) by unavoidable imperfections in the gradient and flow rate profiles produced the HPLC system, and (b) by the changing retention behavior of peptides as a function of solvent composition. Furthermore, a user of such a system has no way to account for differences in the gradient and flow rate profiles produced by their own instrument.
This researcher’s lab has recently developed a new and easy way to fully account for differences in the gradient and flow rate profiles produced by different HPLC instruments. They run a set of 15 standards to “back-calculate” what the effective gradient and flow rate profiles must have been to give their gradient retention times. Those profiles can then be used to very accurately calculate the retention of any other compound for which its isocratic retention vs. solvent composition relationship is known. It enables accurate retention prediction on virtually any HPLC instrument, with any gradient, flow rate, and column dimensions with unprecedented accuracy. This project will measure the isocratic retention times of a large number of peptides (~1000) in order to build a model to predict the isocratic retention vs. solvent composition relationships of peptides based on amino acid sequence. Then, using the new back-calculation methodology, the gradient retention can be predicted very accurately.