Prediction of protein retention in ion-exchange systems using molecular descriptors obtained from crystal structure.
In this paper, a novel approach is described for the a priori prediction of protein retention in ion exchange systems. Quantitative structure retention relationship (QSRR) models based on a genetic algorithm/partial least squares approach were developed using experimental chromatographic data in concert with molecular descriptors computed using protein crystal structures. The resulting QSRR models were well-correlated, with cross-validated r2 values of 0.938 and 0.907, and the predictive power of these models was demonstrated using proteins not included in the derivation of the models. Importantly, these models were able to predict selectivity reversals observed with two different stationary phase materials. To our knowledge, this is the first published example of predictive QSRR models of protein retention based on crystal structure data.
Mazza
C B
CB
Department of Chemical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
Sukumar
N
N
Breneman
C M
CM
Cramer
S M
SM
D002852:Chromatography, Ion Exchange; D006801:Humans; D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D011506:Proteins