Thomas Mietzner

A fast and efficient method to generate biologically relevant conformations.

SummaryMutual binding between a ligand of low molecular weight and its macromolecular receptor demands structural complementarity of both species at the recognition site. To predict binding properties of new molecules before synthesis, information about possible conformations of drug molecules at the active site is required, especially if the 3D structure of the receptor is not known. The statistical analysis of small-molecule crystal data allows one to elucidate conformational preferences of molecular fragments and accordingly to compile libraries of putative ligand conformations. A comparison of geometries adopted by corresponding fragments in ligands bound to proteins shows similar distributions in conformation space. We have developed an automatic procedure that generates different conformers of a given ligand. The entire molecule is decomposed into its individual ring and open-chain torsional fragments, each used in a variety of favorable conformations. The latter ones are produced according to the library information about conformational preferences. During this building process, an extensive energy ranking is applied. Conformers ranked as energetically favorable are subjected to an optimization in torsion angle space. During minimization, unfavorable van der Waals interactions are removed while keeping the open-chain torsion angles as close as possible to the experimentally most frequently observed values. In order to assess how well the generated conformers map conformation space, a comparison with experimental data has been performed. This comparison gives some confidence in the efficiency and completeness of this approach. For some ligands that had been structurally characterized by protein crystallography, the program was used to generate sets of some 10 to 100 conformers. Among these, geometries are found that fall convincingly close to the conformations actually adopted by these ligands at the binding site.

Different approaches toward an automatic structural alignment of drug molecules: Applications to sterol mimics, thrombin and thermolysin inhibitors

SummaryA relative comparison of the binding properties of different drug molecules requires their mutual superposition with respect to various alignment criteria. In order to validate the results of different alignment methods, the crystallographically observed binding geometries of ligands in the pocket of a common protein receptor have been used. The alignment function in the program SEAL that calculates the mutual superposition of molecules has been optimized with respect to these references. Across the reference data set, alignments could be produced that show mean rms deviations of approximately 1 Å compared to the experimental situation. For structures with obvious skeletal similarities a multiple-flexible fit, linking common pharmacophoric groups by virtual springs, has been incorporated into the molecular mechanics program MOMO. In order to combine conformational searching with comparative alignments, the optimized SEAL approach has been applied to sets of conformers generated by MIMUMBA, a program for conformational analysis. Multiple-flexible fits have been calculated for inhibitors of ergosterol biosynthesis. Sets of different thrombin and thermolysin inhibitors have been conformationally analyzed and subsequently aligned by a combined MIMUMBA/SEAL approach. Since for these examples crystallographic data on their mutual alignment are available, an objective assessment of the computed results could be performed. Among the generated conformers, one geometry could be selected for the thrombin and thermolysin inhibitors that approached reasonably well the experimentally observed alignment.

Methodological developments and strategies for a fast flexible superposition of drug-size molecules

An alternative to experimental high through-put screening is the virtual screening of compound libraries on the computer. In absence of a detailed structure of the receptor protein, candidate molecules are compared with a known reference by mutually superimposing their skeletons and scoring their similarity. Since molecular shape highly depends on the adopted conformation, an efficient conformational screening is performed using a knowledge-based approach. A comprehensive torsion library has been compiled from crystal data stored in the Cambridge Structural Database. For molecular comparison a strategy is followed considering shape associated physicochemical properties in space such as steric occupancy, electrostatics, lipophilicity and potential hydrogen-bonding. Molecular shape is approximated by a set of Gaussian functions not necessarily located at the atomic positions. The superposition is performed in two steps: first by a global alignment search operating on multiple rigid conformations and then by conformationally relaxing the best scored hits of the global search. A normalized similarity scoring is used to allow for a comparison of molecules with rather different shape and size. The approach has been implemented on a cluster of parallel processors. As a case study, the search for ligands binding to the dopamine receptor is given.

SUPERPOSITION OF MOLECULES : ELECTRON DENSITY FITTING BY APPLICATION OF FOURIER TRANSFORMS

In this article a new method is described to superimpose molecules using a crystallographic Fourier transform approach. Superimposed molecules, among other purposes, serve as a basis for three‐dimensional (3D) QSAR analyses in drug design and therefore an objective and reproducible method of molecule alignment is of major importance. Fourier data are generated for hypothetical crystals of cubic symmetry for the compounds under consideration. A Patterson‐density‐based similarity index is used to optimize rotational alignment of the molecules. After optimization of rotational orientation, an electron density derived similarity index is used to further optimize overlap of electron density as a function of translation of the molecules. Both similarity indices are maximized by a simple optimization routine, thus enabling automated superposition. The use of Fourier space offers several advantages. First, rotational and translational parameters can be optimized separately, thus providing a small parameter space. Second, a limited number of data already provide an adequate, continuous description of the electron (or Patterson) density distribution. Third, crystallography provides simple methods to calculate the Fourier transforms that are needed. The resolution of the Patterson (electron) density representation used for superposition can be varied in a straightforward manner. Results are shown for the superposition of two antiviral agents, 2rs1 and 2r04; the dihydrofolate reductase ligands, methotrexate and dihydrofolate; and a set of three ϵ‐thrombin inhibitors.

Inhibitors of Plasmodial Serine Hydroxymethyltransferase (SHMT): Cocrystal Structures of Pyrazolopyrans with Potent Blood- and Liver-Stage Activities.

Several of the enzymes related to the folate cycle are well-known for their role as clinically validated antimalarial targets. Nevertheless for serine hydroxymethyltransferase (SHMT), one of the key enzymes of this cycle, efficient inhibitors have not been described so far. On the basis of plant SHMT inhibitors from an herbicide optimization program, highly potent inhibitors of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) SHMT with a pyrazolopyran core structure were identified. Cocrystal structures of potent inhibitors with PvSHMT were solved at 2.6 Å resolution. These ligands showed activity (IC50/EC50 values) in the nanomolar range against purified PfSHMT, blood-stage Pf, and liver-stage P. berghei (Pb) cells and a high selectivity when assayed against mammalian cell lines. Pharmacokinetic limitations are the most plausible explanation for lack of significant activity of the inhibitors in the in vivo Pb mouse malaria model.

A new herbicidal site of action: Cinmethylin binds to acyl-ACP thioesterase and inhibits plant fatty acid biosynthesis

The prevalent occurrence of herbicide resistant weeds increases the necessity for new site of action herbicides for effective control as well as to relax selection pressure on the known sites of action. As a consequence, interest increased in the unexploited molecule cinmethylin as a new solution for the control of weedy grasses in cereals. Therefore, the mechanism of action of cinmethylin was reevaluated. We applied the chemoproteomic approach cellular Target Profiling™ from Evotec to identify the cinmethylin target in Lemna paucicostata protein extracts. We found three potential targets belonging to the same protein family of fatty acid thioesterases (FAT) to bind to cinmethylin with high affinity. Binding of cinmethylin to FAT proteins from Lemna and Arabidopsis was confirmed by fluorescence-based thermal shift assay. The plastid localized enzyme FAT plays a crucial role in plant lipid biosynthesis, by mediating the release of fatty acids (FA) from its acyl carrier protein (ACP) which is necessary for FA export to the endoplasmic reticulum. GC-MS analysis of free FA composition in Lemna extracts revealed strong reduction of unsaturated C18 as well as saturated C14, and C16 FAs upon treatment with cinmethylin, indicating that FA release for subsequent lipid biosynthesis is the primary target of cinmethylin. Lipid biosynthesis is a prominent target of different herbicide classes. To assess whether FAT inhibition constitutes a new mechanism of action within this complex pathway, we compared physiological effects of cinmethylin to different ACCase and VLCFA synthesis inhibitors and identified characteristic differences in plant symptomology and free FA composition upon treatment with the three herbicide classes. Also, principal component analysis of total metabolic profiling of treated Lemna plants showed strong differences in overall metabolic changes after cinmethylin, ACCase or VLCFA inhibitor treatments. Our results identified and confirmed FAT as the cinmethylin target and validate FAT inhibition as a new site of action different from other lipid biosynthesis inhibitor classes.

A Systemsatic Approach to Finding New Lead Structures Having Biological Activity

The development of a new drug is an enormously largescale and expensive process. Thus, computer simulation methods become to play an increasing role in the development of new pharmacologically active compounds. Most of the commercial software presently used, comes from the U.S.; their deficits have become more and more obvious during the last years. Several methods have been developed in our project to alleviate these problems. The search for new lead structures starts with analyzing large databases of compounds (several hundreds of thousands up to several millions of compounds) zeroing into a few promising structures by increasing sophistication of structure representation. Due to the large number of chemical compounds, a systematic scheme for representing structures was developed: The starting point is the constitution, followed by calculation of the 3D structure, then including conformational flexibility. At each step, a variety of chemical properties can be taken into consideration. In addition, new programs have been developed for the treatment of conformational flexibility. The methods presented are also useful for other areas of application dealing with chemical information. Thus, it was shown that one of these new structure representations is suitable for treating problems in combinatorial synthesis. Neural networks and genetic algorithms are highly important for the investigation of the correlation between structure and biological activity. Complex relationships and huge amounts of data can be processed by these methods. Implementation of these procedures on highly parallel computers has proved that datasets of several hundreds of thousands of structures can be treated with acceptable computation times.