Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Abstract

Drug-target residence time (t = 1/k off , where k off is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, k off and activation free energy of dissociation (ΔG ≠ off ). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer's disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development.thermodynamics | flexible docking | metastable states | transition states T raditionally, drug discovery is driven by the idea that binders with higher binding affinity to a target should be more efficacious than those with lower binding affinity to the same target (1). It is obvious that the efficacy of a drug is not only associated with thermodynamics but also related to the binding kinetics between the drug and a defined target (2). Numerous examples demonstrated that drug efficacy does not always linearly correlate with binding affinity (3). Therefore, an affinity-based drug discovery approach is less than complete, and an emerging paradigm emphasizing both thermodynamics and kinetics of drug action has been widely recognized and appreciated in drug discovery (1). In particular, ligand-receptor binding kinetics (BK), which have been overlooked in traditional drug discovery approaches, are unprecedentedly emphasized in almost all steps along the drug discovery and development pipeline (1, 4, 5). Indeed, a statistical analysis on existing drugs demonstrated that the BK profile can be a key differentiator between different drugs (6). Drug-target residence time or dissociative half-life (t 1/2 = 0.693/k off ) has become an important index in lead optimization (4). Thus, the BK-based paradigm will be promising in discovering better-or best-in-class drugs (1).Like the experimental approaches for drug discovery, the current computational drug design methods mainly emphasize binding affinity (7-11). Nevertheless, despite more than 30 y of effort, predicting binding free energies for ligands interacting to targets with sufficient accuracy is stil...

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