HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study.
We describe the application of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel. Models based on a molecular fragment-based descriptor set achieve an accuracy of 85-90% in predicting whether the IC(50) of a 'blind' set of compounds is <1 microM. Analysis of the models provides a 'meta-SAR', which predicts a pharmacophore of two hydrophobic features, one preferably aromatic and one preferably nitrogen-containing, with a protonatable nitrogen asymmetrically situated between them. Our experience of the approach suggests that it is robust, and requires limited scientist input to generate valuable predictive results and structural understanding of the target.
Amedis Pharmaceuticals, Unit 162 Cambridge Science Park, Milton Road, Cambridge, UK.
William
W
Bains
Antranig
A
Basman
Cat
C
White
D000818:Animals; D001665:Binding Sites; D027682:Cation Transport Proteins / Q000737:chemistry / Q000378:metabolism*; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; D006321:Heart / Q000502:physiology; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D015221:Potassium Channels / Q000737:chemistry / Q000378:metabolism* / Q000502:physiology; D024642:Potassium Channels, Voltage-Gated; D012372:ROC Curve
D027682:Cation Transport Proteins; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human; C092509:KCNH6 protein, human; D015221:Potassium Channels; D024642:Potassium Channels, Voltage-Gated