Biomedical subjects

G Klebe

Publications and source records attributed to G Klebe.

At least 19 recordsLinked to original sources

PubMed2000

Knowledge-based scoring function to predict protein-ligand interactions.

The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.

Artificial Intelligence↗

PubMed2000

Screening orthologs as an important variable in crystallization: preliminary X-ray diffraction studies of the tRNA-modifying enzyme S-adenosyl-methionine:tRNA ribosyl transferase/isomerase.

The genes encoding the tRNA-modifying enzyme S-adenosylmethionine:tRNA ribosyl transferase/isomerase (QueA) from 12 eubacterial sources were overexpressed in Escherichia coli and the resulting products were purified to homogeneity and subjected to crystallization trials. Using the hanging-drop vapour-diffusion method, crystals suitable for X-ray diffraction experiments were only obtained for the queA gene product from Bacillus subtilis. The crystals belong to the space group P422, with unit-cell parameters a = b = 100.7, c = 150.9 A. Using highly focused synchrotron radiation from the EMBL/ESRF beamline ID13 (Grenoble, France), diffraction to at least 3.2 A could be achieved. A selenomethionyl derivative of the protein was prepared and crystallized for future multiwavelength anomalous diffraction (MAD) experiments.

Bacteria↗

PubMed2000

The structure of human aldose reductase bound to the inhibitor IDD384.

The crystallographic structure of the complex between human aldose reductase (AR2) and one of its inhibitors, IDD384, has been solved at 1.7 A resolution from crystals obtained at pH 5.0. This structure shows that the binding of the inhibitor's hydrophilic head to the catalytic residues Tyr48 and His110 differs from that found previously with porcine AR2. The difference is attributed to a change in the protonation state of the inhibitor (pK(a) = 4.52) when soaked with crystals of human (at pH 5.0) or pig lens AR2 (at pH 6.2). This work demonstrates how strongly the detailed binding of the inhibitor's polar head depends on its protonation state.

Aldehyde Reductase↗

PubMed1999

Mutagenesis and crystallographic studies of Zymomonas mobilis tRNA-guanine transglycosylase to elucidate the role of serine 103 for enzymatic activity.

The tRNA modifying enzyme tRNA-guanine transglycosylase (TGT) is involved in the exchange of guanine in the first position of the anticodon with preQ1 as part of the biosynthesis of the hypermodified base queuine (Q). Mutation of Ser90 to an alanine in Escherichia coli TGT leads to a dramatic reduction of enzymatic activity (Reuter, K. et al. (1994) Biochemistry 33, 7041-7046). To further clarify the role of this residue in the catalytic center, we have mutated the corresponding Ser103 of the crystallizable Zymomonas mobilis TGT into alanine. The crystal structure of a TGT(S103A)/preQ1 complex combined with biochemical data presented in this paper suggest that Ser103 is essential for substrate orientation in the TGT reaction.

Crystallography↗

PubMed1999

Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa.

Three-dimensional quantitative structure-activity relationship (3D QSAR) methods were applied using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities (Ki values) toward thrombin, trypsin, and factor Xa to yield statistically reliable models of good predictive power. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported CoMSIA approach (comparative molecular similarity indices analysis). CoMSIA produced significantly better results for all correlations. Furthermore, in contrast to CoMFA, CoMSIA is not sensitive to changes in orientation of the superimposed molecules in the lattice. The correlation results obtained by CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The advantage of this feature is demonstrated using the maps to design new molecules. Finally, the CoMSIA method was applied to elucidate structural features among ligands which are responsible for affinity differences toward thrombin and trypsin. These selectivity-determining features were interpreted graphically in terms of spatial regions responsible for affinity discrimination. Such indicators are highly informative for the lead optimization process with respect to selectivity enhancement.

Binding Sites↗

PubMed1999

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.

Algorithms↗

PubMed1999

Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries.

Comparative molecular field analysis has been applied to a data set of thermolysin inhibitors. Fields expressed in terms of molecular similarity indices (CoMSIA) have been used instead of the usually applied Lennard-Jones- and Coulomb-type potentials (CoMFA). Five different properties, assumed to cover the major contributions responsible for ligand binding, have been considered: steric, electrostatic, hydrophobic, and hydrogen-bond donor or acceptor properties. The statistical evaluation of the field properties by PLS analysis reveals a similar predictive potential to CoMFA. However, significantly improved and easily interpretable contour maps are obtained. The features in these maps intuitively suggest where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity. They can also be interpreted with respect to the known structural protein environment of thermolysin. Most of the highlighted regions in the maps are mirrored by features in the surrounding environment required for binding. Using the derived correlation model, different members of a combinatorial library designed for thermolysin inhibition have been scored for affinity. The results obtained demonstrate the prediction power of the CoMSIA method.

Hydrogen Bonding↗

PubMed1999

Improving macromolecular electrostatics calculations.

Electrostatic interactions play a key role in many aspects of protein engineering. Consequently, much effort has been put into the design of software for calculating electrostatic fields around macromolecules. We show that optimization of hydrogen bonding networks can improve both the results of pK(a) calculations and the results of electrostatic calculations performed by commonly used programs such as DelPhi. Further optimization can often be achieved by flipping the side chains of asparagine, histidine and glutamine around their chi2, chi2 and chi3 torsion angles, respectively, when this improves the local hydrogen bonding network. These optimizations are applied to some well characterized proteins: BPTI, hen egg white lysozyme and superoxide dismutase. A search for flipped residues in the PDB revealed that significant improvements in electrostatic calculations in or near the active site of enzymes can be expected for about one quarter of all enzymes in the PDB.

Animals↗

PubMed1999

Inhibitors of aspartic proteases in human diseases: molecular modeling comes of age.

The family of aspartic proteases such as cathepsin D, gastricin, pepsin, renin, HIV protease and others have been the subject of molecular modeling in the field of drug design in the last years. The first aspartic protease inhibitor was reported thirty years ago as a renin inhibitor. The success of HIV protease inhibitors in preventing progression to AIDS was based on the transition state analogs of renin inhibitors. Taking these three decades into consideration, an astonishing variety of chemical classes, in vitro and in vivo activities and species specificities of inhibitors of aspartic proteases have been reported. Especially inhibitors of renin, HIV protease and secreted aspartic protease of Candida albicans are covered.

Aspartic Acid Endopeptidases↗

PubMed1998

FLEXS: a method for fast flexible ligand superposition.

If no structural information about a particular target protein is available, methods of rational drug design try to superimpose putative ligands with a given reference, e.g., an endogenous ligand. The goal of such structural alignments is, on the one hand, to approximate the binding geometry and, on the other hand, to provide a relative ranking of the ligands with respect to their similarity. An accurate superposition is the prerequisite of subsequent exploitation of ligand data by either 3D QSAR analyses, pharmacophore hypotheses, or receptor modeling. We present the automatic method FLEXS for structurally superimposing pairs of ligands, approximating their putative binding site geometry. One of the ligands is treated as flexible, while the other one, used as a reference, is kept rigid. FLEXS is an incremental construction procedure. The molecules to be superimposed are partitioned into fragments. Starting with placements of a selected anchor fragment, computed by two alternative approaches, the remaining fragments are added iteratively. At each step, flexibility is considered by allowing the respective added fragment to adopt a discrete set of conformations. The mean computing time per test case is about 1:30 min on a common-day workstation. FLEXS is fast enough to be used as a tool for virtual ligand screening. A database of typical drug molecules has been screened for potential fibrinogen receptor antagonists. FLEXS is capable of retrieving all ligands assigned to platelet aggregation properties among the first 20 hits. Furthermore, the program suggests additional interesting candidates, likely to be active at the same receptor. FLEXS proves to be superior to commonly used retrieval techniques based on 2D fingerprint similarities. The accuracy of computed superpositions determines the relevance of subsequently performed ligand analyses. In order to validate the quality of FLEXS alignments, we attempted to reproduce a set of 284 mutual superpositions derived from experimental data on 76 protein-ligand complexes of 14 proteins. The ligands considered cover the whole range of drug-size molecules from 18 to 158 atoms (PDB codes: 3ptb, 2er7). The performance of the algorithm critically depends on the sizes of the molecules to be superimposed. The limitations are clearly demonstrated with large peptidic inhibitors in the HIV and the endothiapepsin data set. Problems also occur in the presence of multiple binding modes (e.g., elastase and human rhinovirus). The most convincing results are achieved with small- and medium-sized molecules (as, e.g., the ligands of trypsin, thrombin, and dihydrofolate reductase). In more than half of the entire test set, we achieve rms deviations between computed and observed alignment of below 1.5 A. This underlines the reliability of FLEXS-generated alignments.

Algorithms↗

PubMed1996

A fast flexible docking method using an incremental construction algorithm.

We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is almost always found among the highest-ranking conformations computed.

Algorithms↗

PubMed1995

Synthesis, structure and antitumor activity of new benz[d,e]isoquinoline-1,3-diones.

New benz[d,e]isoquinoline-1,3-diones related to mitonafide (CAS 54824-17-8) and amonafide (CAS 69408-81-7) with double substitution on the chromophore and branched side chains have been synthesized and their biological activity determined. Molecular modeling studies of 3a based on X-ray crystallographic data of mitonafide have shown that the aromatic system intercalates between GC steps of DNA. The in vitro cytotoxic test data using CX-1 and LX-1 cells showed higher cytotoxic activities in disubstituted derivatives compared to both lead compounds. Some of the compounds have been selected for in vivo test using L1210 tumor cells and CX-1 cells. Two of them (3b and 3j) have shown promising activity as good candidates for clinical development.

Adenine↗

PubMed1994

Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.

An alternative approach is reported to compute property fields based on similarity indices of drug molecules that have been brought into a common alignment. The fields of different physicochemical properties use a Gaussian-type distance dependence, and no singularities occur at the atomic positions. Accordingly, no arbitrary definitions of cutoff limits and deficiencies due to different slopes of the fields are encountered. The fields are evaluated by a PLS analysis similar to the CoMFA formalism. Two data sets of steroids binding to the corticosteroid-binding-globulin and thermolysin inhibitors were analyzed in terms of the conventional CoMFA method (Lennard-Jones and Coulomb potential fields) and the new comparative molecular similarity indices analysis (CoMSIA). Models of comparative statistical significance were obtained. Field contribution maps were produced for the different models. Due to cutoff settings in the CoMFA fields and the steepness of the potentials close to the molecular surface, the CoMFA maps are often rather fragmentary and not contiguously connected. This makes their interpretation difficult. The maps obtained by the new CoMSIA approach are superior and easier to interpret. Whereas the CoMFA maps denote regions apart from the molecules where interactions with a putative environment are to be expected, the CoMSIA maps highlight those regions within the area occupied by the ligand skeletons that require a particular physicochemical property important for activity. This is a more significant guide to trace the features that really matter especially with respect to the design of novel compounds.

Models, Molecular↗

PubMed1994

The use of composite crystal-field environments in molecular recognition and the de novo design of protein ligands.

Small molecule crystal data have been retrieved from the Cambridge Crystallographic Database to compile composite crystal-field environments about different functional groups, which also occur in proteins and nucleotides. Their spatial distribution can be used to map-out putative interaction sites, e.g. about amino acid residues oriented towards the binding site of a given protein. Although influenced by packing forces, these composite environments show systematic patterns which reflect preferred interaction geometries of the functional groups under consideration with neighboring groups, e.g. hydrogen bonding partners. Similar but substantially less detailed distributions have been obtained from crystallographically determined ligand/protein complexes, which demonstrate that the properties observed in low-molecular weight structures are representative also for the sought after spatial orientation of interactions between ligands and their receptor proteins. The crystallographically determined binding geometries of three inhibitor/enzyme complexes are compared with the distributions of putative interaction sites predicted from corresponding composite field environments. In some cases, the observed positions of ligand atoms interacting with the proteins coincide with a region which is also frequently occupied by similar bonding partners in organic crystal structures, however, interaction geometries are also found which fall close to the limits of the ranges observed in the small molecule reference data. The information contained in the different composite crystal-field environments can be translated into rules which serve as guide-lines for automatic docking of small molecule fragments into the active site of proteins.

Binding Sites↗

PubMed1994

A fast and efficient method to generate biologically relevant conformations.

Mutual 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 conformations 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.

Adenosine Monophosphate↗

PubMed1994

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

A 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 A 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.

Amino Acid Sequence↗

PubMed1993

On the prediction of binding properties of drug molecules by comparative molecular field analysis.

Comparative molecular field analysis (CoMFA) has been applied to three different data sets of drug molecules binding to human rhinovirus 14 (HRV14), thermolysin and renin, respectively. Different structural alignments have been tested to predict binding properties. An alignment based on crystallographically determined coordinates of the inhibitors bound to the proteins has been compared with alignments obtained from multiple-fit and field-fit procedures. These methods are commonly used for systems where no reference to protein structural data is available. For HRV14, two different models, one based on experimental evidence and one based on a hypothetical alignment reveal moderate predictions of the binding constant of comparable quality. For thermolysin, hypothetical alignments allow a substantially better prediction than an alignment based on experimental evidence. The prediction of binding properties (expressed as delta G, delta H, and delta S) of renin inhibitors, which were aligned on the basis of crystallographic data from related inhibitors bound to the aspartyl protease endothiapepsin, gives evidence that only enthalpies (delta H) and not free enthalpies (delta G) or binding constants can be properly predicted by comparative molecular field analysis.

Amino Acid Sequence↗

PubMed1992

Sesquiterpenoids from the roots of Homalomena aromatica.

From roots of Homalomena aromatica three new sesquiterpene alcohols, 1 beta, 4 beta, 7 alpha-trihydroxyeudesmane, homalomenol A, and homalomenol B were isolated together with oplopanone, oplodiol and bullatantriol. The structures of the new constituents were elucidated by spectroscopic investigations and chemical transformations.

Magnetic Resonance Spectroscopy↗