We believe that a solution of the docking problem may be achieved by matching a simple model of molecular recognition with an efficient search procedure. The necessary ingredients of a molecular recognition model include only steric and hydrogen-bond interaction terms. Although these terms are not necessarily sufficient to predict binding affinity, they describe ligand-protein interactions faithfully enough to enable a docking program to predict the structure of the bound ligand. This docking strategy thus provides an important tool for the interdisciplinary field of rational drug design.

A new procedure for molecular replacement is presented in which an ef®cient six-dimensional search is carried out using an evolutionary optimization algorithm. In this procedure, a population of initially random molecular-replacement solutions is iteratively optimized with respect to the correlation coef®cient between observed and calculated structure factors. The sensitivity and reliability of the method is enhanced by uniform sampling of the rotational-search space and the use of continuously variable rotational and translational parameters. The process is several orders of magnitude faster than a systematic six-dimensional search, and comparisons show that it can identify solutions using signi®cantly less accurate or less complete search models than is possible with two existing molecular-replacement methods. A program incorporating the method, EPMR, allows the rapid and highly automated solution of molecular-replacement problems involving single or multiple molecules in the asymmetric unit. EPMR has been used to solve a number of dif®cult molecular-replacement problems.

Several protein engineering approaches were combined to optimize the selectivity and activity of Vibrio fluvialis aminotransferase (Vfat) for the synthesis of (3S,5R)-ethyl 3-amino-5-methyloctanoate; a key intermediate in the synthesis of imagabalin, an advanced candidate for the treatment of generalized anxiety disorder. Starting from wild-type Vfat, which had extremely low activity catalyzing the desired reaction, we engineered an improved enzyme with a 60-fold increase in initial reaction velocity for transamination of (R)-ethyl 5-methyl 3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate. To achieve this, <450 variants were screened, which allowed accurate assessment of enzyme performance using a low-throughput ultra performance liquid chromatography assay. During the course of this work, crystal structures of Vfat wild type and an improved variant (Vfat variant r414) were solved and they are reported here for the first time. This work also provides insight into the critical residues for substrate specificity for the transamination of (R)-ethyl 5-methyl 3-oxooctanoate and structurally related β-ketoesters.

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as 'soft' and 'hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.

The identification of phospholipidosis (PPL) during preclinical testing in animals is a recognized problem in the pharmaceutical industry. Depending on the intended indication and dosing regimen, PPL can delay or stop development of a compound in the drug discovery process. Therefore, for programs and projects where a PPL finding would have adverse impact on the success of the project, it would be desirable to be able to rapidly identify and screen out those compounds with the potential to induce PPL as early as possible. Currently, electron microscopy is the gold standard method for identifying phospholipidosis, but it is low-throughput and resource-demanding. Therefore, a low-cost, high-throughput screening strategy is required to overcome these limitations and be applicable in the drug discovery cycle. A recent publication by Ploemen et al. (Exp. Toxicol. Pathol. 2004, 55, 347-55) describes a method using the computed physicochemical properties pKa and ClogP as part of a simple calculation to determine a compound's potential to induce PPL. We have evaluated this method using a set of 201 compounds, both public and proprietary, with known in vivo PPL-inducing ability and have found the overall concordance to be 75%. We have proposed simple modifications to the model rules, which improve the model's concordance to 80%. Finally, we describe the development of a Bayesian model using the same compound set and found its overall concordance to be 83%.