Gerd Folkers

Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to discriminate about 7 out of 10 true hits from a random database of 990 ligands. The use of consensus lists common to two or three scoring functions clearly enhances hit rates among the top 5% scorers from 10% (single scoring) to 25-40% (double scoring) and up to 65-70% (triple scoring). However, in all tested cases, no clear relationships could be found between docking and ranking accuracies. Moreover, predicting the absolute binding free energy of true hits was not possible whatever docking accuracy was achieved and scoring function used. As the best docking/consensus scoring combination varies with the selected target and the physicochemistry of target-ligand interactions, we propose a two-step protocol for screening large databases: (i) screening of a reduced dataset containing a few known ligands for deriving the optimal docking/consensus scoring scheme, (ii) applying the latter parameters to the screening of the entire database.

The influence of physicochemical properties, including lipophilicity, H-bonding capacity and molecular size and shape descriptors on brain uptake has been investigated using a selection of marketed CNS and CNS-inactive drugs. It is demonstrated that the polar surface area of a drug can be used as a suitable descriptor for the drugs' H-bonding potential. A combination of a H-bonding and a molecular size descriptor, i.e., the major components of lipophilicity and permeability, avoiding knowledge of distribution coefficients, is proposed to estimate brain penetration potential of new drug candidates. Previously reported experimental surface activity data appear to be strongly correlated to molecular size of the drug compounds. Present analysis offers a modern basis for property-based design and targeting of CNS drugs.

The understanding of molecular recognition processes of small ligands and biological macromolecules requires a complete characterization of the binding energetics and correlation of thermodynamic data with interacting structures involved. A quantitative description of the forces that govern molecular associations requires determination of changes of all thermodynamic parameters, including free energy of binding (ΔG), enthalpy (ΔH), and entropy (ΔS) of binding and the heat capacity change (ΔCp). A close insight into the binding process is of significant and practical interest, since it provides the fundamental know-how for development of structure-based molecular design strategies. The only direct method to measure the heat change during complex formation at constant temperature is provided by isothermal titration calorimetry (ITC). With this method one binding partner is titrated into a solution containing the interaction partner, thereby generating or absorbing heat. This heat is the direct observable that can be quantified by the calorimeter. The use of ITC has been limited due to the lack of sensitivity, but recent developments in instrument design permit to measure heat effects generated by nanomol (typically 10–100) amounts of reactants. ITC has emerged as the primary tool for characterizing interactions in terms of thermodynamic parameters. Because heat changes occur in almost all chemical and biochemical processes, ITC can be used for numerous applications, e.g., binding studies of antibody–antigen, protein–peptide, protein–protein, enzyme–inhibitor or enzyme–substrate, carbohydrate–protein, DNA–protein (and many more) interactions as well as enzyme kinetics. Under appropriate conditions data analysis from a single experiment yields ΔH, KB, the stoichiometry (n), ΔG and ΔS of binding. Moreover, ITC experiments performed at different temperatures yield the heat capacity change (ΔCp). The informational content of thermodynamic data is large, and it has been shown that it plays an important role in the elucidation of binding mechanisms and, through the link to structural data, also in rational drug design. In this review we will present a comprehensive overview to ITC by giving some historical background to calorimetry, outline some critical experimental and data analysis aspects, discuss the latest developments, and give three recent examples of studies published with respect to macromolecule–ligand interactions that have utilized ITC technology.