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Detalle de: Single-Molecule Pulling Simulations Can Discern Active from Inactive Enzyme Inhibitors

Title Single-Molecule Pulling Simulations Can Discern Active from Inactive Enzyme Inhibitors
Abstract

Understanding ligand-protein recognition and interaction processes is of primary importance for structure-based drug design. Traditionally, several approaches combining docking and molecular dynamics (MD) simulations have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein-ligand complex can be well predicted by computational methods, it is challenging to rank a series of analogues in a consistent fashion with biological data. In the unique beta-hydroxyacyl-ACP dehydratase of Plasmodium falciparum (PfFabZ), the application of standard molecular docking and MD simulations was partially sufficient to shed light on the activity of previously discovered inhibitors. Complementing docking results with atomistic simulations in the steered molecular dynamics (SMD) framework, we devised an in silico approach to study molecular interactions and to compare the binding characteristics of ligand analogues. We hypothesized an interaction model that both explained the biological activity of known ligands, and provided insight into designing novel enzyme inhibitors. Mimicking single-molecule pulling experiments, we used SMD-derived force profiles to discern active from inactive compounds for the first time. A new compound was designed and its biological activity toward the PfFabZ enzyme predicted. Finally, the computational predictions were experimentally confirmed, highlighting the robustness of the drug design approach presented herein.

Apa Reference Colizzi, F., Perozzo, R., Scapozza, L., Recanatini, M. & Cavalli, A. (2010). Single-Molecule Pulling Simulations Can Discern Active from Inactive Enzyme Inhibitors. Journal of the American Chemical Society, 132(21), 7361-7371. 10.1021/ja100259r
Doi 10.1021/ja100259r
Publication Date 12 May 2010
Publication Type Article
Researcher Francesco Colizzi
Ontology Computational models and simulations, Biomolecular simulation, Drug Discovery and design, Molecular Interactions/Docking
Research Groups
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