Comparative structural connectivity spectra analysis (CoSCoSA) models of steroid binding to the corticosteroid binding globulin.
A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) model was developed that is built by combining NMR spectral information with structural information in a 3D-connectivity matrix. The 3D-connectivity matrix is built by displaying all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts and distances between the carbons. Selected 2D (13)C-(13)C COrrelation SpectroscopY (COSY) (through-bond nearest neighbors) and selected theoretical 2D (13)C-(13)C distance connectivity spectral slices from the 3D-connectivity matrix to produce a relationship among the spectral patterns for 30 steroids binding to corticosteroid binding globulin. We call this technique a comparative structural connectivity spectra analysis (CoSCoSA) modeling. A CoSCoSA principal component linear regression model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra principal components (PCs) had an r(2) of 0.96 and a leave-one-out (LOO) cross-validation q(2) of 0.92. A CoSCoSA parallel distributed artificial neural network (PD-ANN) model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra had an r(2) of 0.96, a leave-three-out q(3)(2) of 0.78, and a leave-ten-out q(10)(2) of 0.73. CoSCoSA modeling attempts to uniquely combine the quantum mechanics information from the NMR chemical shifts with internal molecular atom-to-atom distances into an accurate modeling technique. The CoSCoSA modeling technique has the flexibility and accuracy to outperform the cross-validated variance q(2) of previously published quantitative structure-activity relationship (QSAR), quantitative spectral data-activity relationship (QSDAR), self-organizing map (SOM), and electrotopological state (E-state) models.
Beger
Richard D
RD
Division of Chemistry, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079-9502, USA. rbeger@nctr.fda.gov
Buzatu
Dan A
DA
Wilkes
Jon G
JG
Lay
Jackson O
JO
D001665:Binding Sites; D003198:Computer Simulation; D066298:In Vitro Techniques; D009682:Magnetic Resonance Spectroscopy; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D012985:Software Design; D013256:Steroids / Q000737:chemistry* / Q000378:metabolism*; D014156:Transcortin / Q000378:metabolism*
D013256:Steroids; D014156:Transcortin