In higher organisms the formation of the steroid scaffold is catalysed exclusively by the membrane-bound oxidosqualene cyclase (OSC; lanosterol synthase). In a highly selective cyclization reaction OSC forms lanosterol with seven chiral centres starting from the linear substrate 2,3-oxidosqualene. Valuable data on the mechanism of the complex cyclization cascade have been collected during the past 50 years using suicide inhibitors, mutagenesis studies and homology modelling. Nevertheless it is still not fully understood how the enzyme catalyses the reaction. Because of the decisive role of OSC in cholesterol biosynthesis it represents a target for the discovery of novel anticholesteraemic drugs that could complement the widely used statins. Here we present two crystal structures of the human membrane protein OSC: the target protein with an inhibitor that showed cholesterol lowering in vivo opens the way for the structure-based design of new OSC inhibitors. The complex with the reaction product lanosterol gives a clear picture of the way in which the enzyme achieves product specificity in this highly exothermic cyclization reaction.

The MM-PBSA approach has become a popular method for calculating binding affinities of biomolecular complexes. Published application examples focus on small test sets and few proteins and, hence, are of limited relevance in assessing the general validity of this method. To further characterize MM-PBSA, we report on a more extensive study involving a large number of ligands and eight different proteins. Our results show that applying the MM-PBSA energy function to a single, relaxed complex structure is an adequate and sometimes more accurate approach than the standard free energy averaging over molecular dynamics snapshots. The use of MM-PBSA on a single structure is shown to be valuable (a) as a postdocking filter in further enriching virtual screening results, (b) as a helpful tool to prioritize de novo design solutions, and (c) for distinguishing between good and weak binders (DeltapIC(50) > or = 2-3), but rarely to reproduce smaller free energy differences.

Two new docking programs FRED (OpenEye Scientific Software) and Glide (Schrödinger, Inc.) in combination with various scoring functions implemented in these programs have been evaluated against a variety of seven protein targets (cyclooxygenase-2, estrogen receptor, p38 MAP kinase, gyrase B, thrombin, gelatinase A, neuraminidase) in order to assess their accuracy in virtual screening. Sets of known inhibitors were added to and ranked relative to a random library of drug-like compounds. Performance was compared in terms of enrichment factors and CPU time consumption. Results and specific features of the two new tools are discussed and compared to previously published results using FlexX (Tripos, Inc.) as a docking engine. In addition, general criteria for the selection of docking algorithms and scoring functions based on binding-site characteristics of specific protein targets are proposed. Figure Enrichment factors obtained with FlexX, Glide and FRED docking engines in combination with different scoring functions for seven selected targets with highly variable binding sites

Crystal structure databases offer ample opportunities to derive small molecule conformation preferences, but the derived knowledge is not systematically applied in drug discovery research. We address this gap by a comprehensive and extendable expert system enabling quick assessment of the probability of a given conformation to occur. It is based on a hierarchical system of torsion patterns that cover a large part of druglike chemical space. Each torsion pattern has associated frequency histograms generated from CSD and PDB data and, derived from the histograms, traffic-light rules for frequently observed, rare, and highly unlikely torsion ranges. Structures imported into the corresponding software are annotated according to these rules. We present the concept behind the tree of torsion patterns, the design of an intuitive user interface for the management and usage of the torsion library, and we illustrate how the system helps analyze and understand conformation properties of substructures widely used in medicinal chemistry.

We present a series of small molecule drug discovery case studies where computational methods were prospectively employed to impact Roche research projects, with the aim of highlighting those methods that provide real added value. Our brief accounts encompass a broad range of methods and techniques applied to a variety of enzymes and receptors. Most of these are based on judicious application of knowledge about molecular conformations and interactions: filling of lipophilic pockets to gain affinity or selectivity, addition of polar substituents, scaffold hopping, transfer of SAR, conformation analysis, and molecular overlays. A case study of sequence-driven focused screening is presented to illustrate how appropriate preprocessing of information enables effective exploitation of prior knowledge. We conclude that qualitative statements enabling chemists to focus on promising regions of chemical space are often more impactful than quantitative prediction.