Walking on the free energy hypersurface of the 18‐crown‐6 ion system using free energy derivatives

Designing molecular complexes using free-energy derivatives from liquid-state integral equation theory

Complex formation between molecules in solution is the key process by which molecular interactions are translated into functional systems. These processes are governed by the binding or free energy of association which depends on both direct molecular interactions and the solvation contribution. A design goal frequently addressed in pharmaceutical sciences is the optimization of chemical properties of the complex partners in the sense of minimizing their binding free energy with respect to a change in chemical structure. Here, we demonstrate that liquid-state theory in the form of the solute-solute equation of the reference interaction site model provides all necessary information for such a task with high efficiency. In particular, computing derivatives of the potential of mean force (PMF), which defines the free-energy surface of complex formation, with respect to potential parameters can be viewed as a means to define a direction in chemical space toward better binders. We illustrate the methodology in the benchmark case of alkali ion binding to the crown ether 18-crown-6 in aqueous solution. In order to examine the validity of the underlying solute-solute theory, we first compare PMFs computed by different approaches, including explicit free-energy molecular dynamics simulations as a reference. Predictions of an optimally binding ion radius based on free-energy derivatives are then shown to yield consistent results for different ion parameter sets and to compare well with earlier, orders-of-magnitude more costly explicit simulation results. This proof-of-principle study, therefore, demonstrates the potential of liquid-state theory for molecular design problems.

Sensitivity Analysis and Charge-Optimization for Flexible Ligands: Applicability to Lead Optimization

Sensitivity analysis and charge-optimization have been suggested as methods to guide the optimization of lead compounds in early-stage drug discovery. However, applications to date have been restricted by the simplifying assumption of a rigid ligand. The present study applies both formalisms to the case of a flexible ligand in a model application to an HIV-protease inhibitor. The results suggest that sensitivity analysis is a fast and robust method for guiding charge changes in both a rigid and a flexible ligand, although its accuracy is limited by the fact that it represents a linear approximation. The more complete quadratic analysis provided by charge-optimization produces unexpected results when the ligand is considered to be flexible. For example, it can yield atomic charges which powerfully stabilize the bound conformation of the ligand relative to the conformation assumed for the free state, thus markedly destabilizing the assumed free conformation. Such results are traceable to the fact that the energy matrix possesses negative eigenvalues. However, optimizing charges under the assumption that the ligand does not change conformation upon binding leads to a set of charges that robustly improve affinity, even when the free conformation is later allowed to vary. Thus, both sensitivity analysis and charge-optimization appear to be useful techniques.

A technique to study molecular recognition in drug design: preliminary application of free energy derivatives to inhibition of a malarial cysteine protease

We present molecular dynamics studies on model complexes of inhibitors of a malarial cysteine protease. The initial model for such complexes came from the model building of the protein using its homology with other cysteine proteases and calculations using DOCK to generate new lead compounds. Some of the initial model-built structures were quite stable for 100 psec of dynamics; others moved significantly from their model-built orientation. We also calculated the free energy derivatives at each atom in the inhibitor, both in water and in the binding site. The results of these calculations suggest directions for the design of new, more potent enzyme inhibitors and agree qualitatively with some of the experimental findings. Nonetheless, we stress that we have only used this methodology in an interpretive rather than a predictive manner.