Prediction of Enzyme Binding:  Human Thrombin Inhibition Study by Quantum Chemical and Artificial Intelligence Methods Based on X-ray Structures

• Published in: Journal of Chemical Information and Computer Sciences 2001-09, Vol.41 (5), p.1286-1294
• Main Authors: Mlinsek, G, Novic, M, Hodoscek, M, Solmajer, T
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Catalytic Domain; Crystallography, X-Ray; Databases, Protein; Drug Design; Humans; In Vitro Techniques; Models, Molecular; Neural Networks (Computer); Quantum Theory; Serine Proteinase Inhibitors - chemistry; Serine Proteinase Inhibitors - pharmacology; Static Electricity; Thrombin - antagonists & inhibitors; Thrombin - chemistry; Thrombin - metabolism
• ISSN: 0095-2338; 1549-960X
• DOI: 10.1021/ci000162e

Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor−enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.