Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.
Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).
Song
Minghu
M
Departments of Chemistry, Mathematics, and Chemical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA.
Breneman
Curt M
CM
Bi
Jinbo
J
Sukumar
N
N
Bennett
Kristin P
KP
Cramer
Steven
S
Tugcu
Nihal
N
D000837:Anion Exchange Resins / Q000737:chemistry*; D002852:Chromatography, Ion Exchange / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D011789:Quantum Theory; D012044:Regression Analysis; D055672:Static Electricity
D000837:Anion Exchange Resins; D011506:Proteins