Neural network methods to predict enzyme inhibitor or...

Details Neural network methods to predict enzyme inhibitor or... Neural network methods to predict enzyme inhibitor or...

2005-05-17

2005-05-17

Knight, Anthony (Department: 2121)

Data processing: artificial intelligence

Neural network

C706S021000, C706S924000

Reexamination Certificate

active

06895396

ABSTRACT:
A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors. The method is in fact able to predict the large binding free energy of the transition state, when trained with less tightly bound inhibitors. The present method is also applicable to prediction of the binding free energy of a ligand to a receptor. The application of this approach to the study of transition state inhibitors and ligands would permit evaluation of chemical libraries of potential inhibitory, agonistic, or antagonistic agents. The method is amenable to incorporation in a computer-readable medium accessible by general-purpose computers.

REFERENCES:
patent: 6185548 (2001-02-01), Schwartz et al.
patent: 6678618 (2004-01-01), Schwartz et al.
patent: 20030014191 (2003-01-01), Agrafiotis et al.
patent: 20030139687 (2003-07-01), Abreu
patent: 20030229456 (2003-12-01), Beger et al.
Bagdassarian et al., Molecular Electrostatic Potential Analysis for Enzymatic Substrates, Competitive Inhibitors, and Transition-State Inhibitors. J. Am. Chem. Soc., 118:8825-36, 1996.
Betts et al., Cytidine Deaminase. The 2-3 Angstrom Crystal Structure of an Enzyme: Transition-state Analog Complex. J. Mol. Biol., 235:635-56, 1994.
Bohm, New Approaches in Molecular Structure Prediction. Biophysical Chemistry, 59:1-32, 1996.
Brusic et al., Prediction of MHC Class II-Binding Peptides Using an Evolutionary Algorithm and Artificial Neural Network. Bioinformatics, 14:121-30, 1998.
Ehrlich and Schramm, Electrostatic Potential Surface Analysis of the Transition State for AMP Nucleosidase and for Formycin 5′-Phosphate, a Transition-State Inhibitor. Biochem., 33:8890-96, 1994.
Frick et al., Binding of Pyrimidin-2-one Ribonucleoside by Cytidine Deaminase as the Transition-State Analogue 3,4-Dihydrouridine and the Contribution of the 4-Hydroxyl Group to Its Binding Affinity. Biochemistry. 28:9423-30, 1989.
Gasteiger et al., Representation of Molecular Electrostatic Potentials by Topological Feature Maps. J. Am. Chem. Soc., 116:4608-20, 1994.
Horenstein and Schramm, Electronic Nature of the Transition State for Nucleoside Hydrolase. A Blueprint for Inhibitor Design. Biochemistry, 32:7089-97, 1993.
Kline and Schramm, Electrostatic Potential Surfaces of the Transition State for AMP Deaminase and for (R)-Coformycin, a Transition State Inhibitor. J. Biol Chem., 269:22385-90, 1994.
So and Richards, Application of Neural Networks: Quantitative Structure-Activity Relationships of the Derivatives of 2,4-Diamino-5-(substituted-benzyl) pyrimidines as DHFR Inhibitors. J. Med. Chem., 35:3201-7, 1992.
Wagener et al., Autocorrelation of Molecular Surface Properties for Modeling Corticosteriod Binding Globulin and Cytoslic Ah Receptor Activity by Neural Networks. J. Am. Chem. Soc., 117:7769-75, 1995.
Weinstein et al., Predictive Statistics and Artificial Intelligence in the U.S. National Cancer Institute's Drug Discovery Program for Cancer and AIDS. Stem Cells, 12:13-22, 1994.

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