Novel approaches to predict the retention of histidine‐containing peptides in immobilized metal‐affinity chromatography

Abstract

The new method lazy learning method-local lazy regression (LLR) was first used to model the quantitative structure-retention relationship (QSRR) for predicting and explaining the retention behaviors of peptides in the nickel column in immobilized metal-affinity chromatography (IMAC). The best multilinear regression (BMLR) method implemented in the CODESSA was used to select the most appropriate molecular descriptors from a large set and build a linear regression model. Based on the selected five descriptors, another two approaches, projection pursuit regression (PPR) and LLR were used to build more accurate QSRR models. The coefficients of determination (R(2)) of the best model developed based on LLR were 0.9446 and 0.9252 for the training set and the test set, respectively. By comparison, it was proved that the novel local learning method LLR was a very promising tool for QSRR modeling with excellent predictive capability for the prediction of imidazole concentration (IMC) values of histidine-containing peptides in IMAC. It could be used in other chromatography research fields and that should facilitate the design and purification of peptides and proteins.