title: Prediction of inhibitor binding free energies by quantum neural networks. Nucleoside analogues binding to trypanosomal nucleoside hydrolase.
abstract: A computational method has been developed to predict inhibitor binding energy for untested inhibitor molecules. A neural network is trained from the electrostatic potential surfaces of known inhibitors and their binding energies. The algorithm is then able to predict, with high accuracy, the binding energy of unknown inhibitors. IU-nucleoside hydrolase from Crithidia fasciculata and the inhibitor molecules described previously [Miles, R. W. Tyler, P. C. Evans, G. Furneaux R. H., Parkin, D. W., and Schramm, V. L. (1999) Biochemistry 38, xxxx-xxxx] are used as the test system. Discrete points on the molecular electrostatic potential surface of inhibitor molecules are input to neural networks to identify the quantum mechanical features that contribute to binding. Feed-forward neural networks with back-propagation of error are trained to recognize the quantum mechanical electrostatic potential and geometry at the entire van der Waals surface of a group of training molecules and to predict the strength of interactions between the enzyme and novel inhibitors. The binding energies of unknown inhibitors were predicted, followed by experimental determination of K(i)() values. Predictions of K(i)() values using this theory are compared to other methods and are more robust in estimating inhibitory strength. The average deviation in estimating K(i)() values for 18 unknown inhibitor molecules, with 21 training molecules, is a factor of 5 x K(i)() over a range of 660 000 in K(i)() values for all molecules. The a posteriori accuracy of the predictions suggests the method will be effective as a guide for experimental inhibitor design.
authors.affiliation: The Department of Physiology and Biophysics, Albert Einstein College of Medicine, Bronx, New York 10461, USA.
authors.forename: B B
authors.initials: BB
authors.lastname: Braunheim
authors.forename: R W
authors.initials: RW
authors.lastname: Miles
authors.forename: V L
authors.initials: VL
authors.lastname: Schramm
authors.forename: S D
authors.initials: SD
authors.lastname: Schwartz
mesh_terms: D000818:Animals; D001665:Binding Sites; D003198:Computer Simulation; D016831:Crithidia fasciculata / Q000201:enzymology*; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000378:metabolism; D006860:Hydrogen Bonding; D006868:Hydrolysis; D009699:N-Glycosyl Hydrolases / Q000037:antagonists & inhibitors* / Q000737:chemistry* / Q000378:metabolism; D016571:Neural Networks, Computer; D011789:Quantum Theory; D012263:Ribonucleosides / Q000737:chemistry* / Q000378:metabolism; D012266:Ribose / Q000737:chemistry / Q000378:metabolism; D055672:Static Electricity; D013329:Structure-Activity Relationship; D013499:Surface Properties; D013816:Thermodynamics
chemical_list: D004791:Enzyme Inhibitors; D012263:Ribonucleosides; D012266:Ribose; D009699:N-Glycosyl Hydrolases; C096466:inosine-uridine hydrolase