Displacement of disordered water molecules from hydrophobic pocket creates enthalpic signature: binding of phosphonamidate to the S1'-pocket of thermolysin.

BACKGROUND: Prerequisite for the design of tight binding protein inhibitors and prediction of their properties is an in-depth understanding of the structural and thermodynamic details of the binding process. A series of closely related phosphonamidates was studied to elucidate the forces underlying their binding affinity to thermolysin. The investigated inhibitors are identical except for the parts penetrating into the hydrophobic S1'-pocket. METHODS: A correlation of structural, kinetic and thermodynamic data was carried out by X-ray crystallography, kinetic inhibition assay and isothermal titration calorimetry. RESULTS AND CONCLUSIONS: Binding affinity increases with larger ligand hydrophobic P1'-moieties accommodating the S1'-pocket. Surprisingly, larger P1'-side chain modifications are accompanied by an increase in the enthalpic contribution to binding. In agreement with other studies, it is suggested that the release of largely disordered waters from an imperfectly hydrated pocket results in an enthalpically favourable integration of these water molecules into bulk water upon inhibitor binding. This enthalpically favourable process contributes more strongly to the binding energetics than the entropy increase resulting from the release of water molecules from the S1'-pocket or the formation of apolar interactions between protein and inhibitor. GENERAL SIGNIFICANCE: Displacement of highly disordered water molecules from a rather imperfectly hydrated and hydrophobic specificity pocket can reveal an enthalpic signature of inhibitor binding.

Statistical potentials and scoring functions applied to protein-ligand binding.

In virtual screening, small-molecule ligands are docked into protein binding sites and their binding affinity is predicted. Knowledge-based, regression-based and first-principle-based methods have been developed to rank computer-generated binding modes. As a result of still existing deficiencies, a best compromise might be the combination of several scoring schemes into a consensus scoring approach.

Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase.

A systematic analysis of the hydrogen-bonding geometry in helices and beta sheets has been performed. The distances and angles between the backbone carbonyl O and amide N atoms were correlated considering more than 1500 protein chains in crystal structures determined to a resolution better than 1.5 A. They reveal statistically significant trends in the H-bond geometry across the different secondary structural elements. The analysis has been performed using Secbase, a modular extension of Relibase (Receptor Ligand Database) which integrates information about secondary structural elements assigned to individual protein structures with the various search facilities implemented into Relibase. A comparison of the mean hydrogen-bond distances in alpha helices and 3(10) helices of increasing length shows opposing trends. Whereas in alpha helices the mean H-bond distance shrinks with increasing helix length and turn number, the corresponding mean dimension in 3(10) helices expands in a comparable series. Comparing similarly the hydrogen-bond lengths in beta sheets there is no difference to be found between the mean H-bond length in antiparallel and parallel beta sheets along the strand direction. In contrast, an interesting systematic trend appears to be given for the hydrogen bonds perpendicular to the strands bridging across an extended sheet. With increasing number of accumulated strands, which results in a growing number of back-to-back piling hydrogen bonds across the strands, a slight decrease of the mean H-bond distance is apparent in parallel beta sheets whereas such trends are obviously not given in antiparallel beta sheets. This observation suggests that cooperative effects mutually polarizing spatially well-aligned hydrogen bonds are present either in alpha helices and parallel beta sheets whereas such influences seem to be lacking in 3(10) helices and antiparallel beta sheets.

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.

Docking ligands onto binding site representations derived from proteins built by homology modelling.

Due to the abundant sequence information available from genome projects, an increasing number of structurally unknown proteins, homologous to examples of known 3D structure, will be discovered as new targets for drug design. Since homology models do not provide sufficient accuracy to apply common drug design tools, a new approach, DragHome, has been developed to dock ligands into such approximate protein models. DragHome combines information from homology modelling with ligand data, used by and derived from 3D quantitative structure-activity relationships (QSAR). The binding-site of a model-built protein is analysed in terms of putative ligand interaction sites and translated via Gaussian functions into a functional binding-site description represented by physico-chemical properties. Ligands to be docked onto these binding-site representations are similarly translated into a description based on Gaussian functions. The docking is computed by optimising the overlap between the functional description of the binding site and the ligand, generating multiple solutions. For a set of different ligands, these solutions are ranked according to the internal similarity consistance among the various ligands in the binding modes obtained from docking. DragHome has been validated at examples for which crystal structures are available: structurally distinct thrombin inhibitors were docked onto models of thrombin generated from serine proteases of 28 to 40 % sequence identity, yielding ligand binding modes with an average RMS deviation of 1.4 A. Mostly the near-native solutions are ranked best. Molecular flexibility of ligands can be considered in terms of pre-calculated multiple conformers. DragHome has been used to automatically generate an alignment of 88 thrombin inhibitors, for which a significant 3D QSAR model could be derived. The contribution maps resulting from this analysis can be interpreted with respect to the surrounding protein model. They highlight inconsistencies and deficiencies present in the model. In future developments, this information could be fed back into a subsequent modelling step to improve the protein model.

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.

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.

Factorising ligand affinity: a combined thermodynamic and crystallographic study of trypsin and thrombin inhibition.

The binding of a series of low molecular weight ligands towards trypsin and thrombin has been studied by isothermal titration calorimetry and protein crystallography. In a series of congeneric ligands, surprising changes of protonation states occur and are overlaid on the binding process. They result from induced pK(a) shifts depending on the local environment experienced by the ligand and protein functional groups in the complex (induced dielectric fit). They involve additional heat effects that must be corrected before any conclusion on the binding enthalpy (DeltaH) and entropy (DeltaS) can be drawn. After correction, trends in both contributions can be interpreted in structural terms with respect to the hydrogen bond inventory or residual ligand motions. For all inhibitors studied, a strong negative heat capacity change (DeltaC(p)) is detected, thus binding becomes more exothermic and entropically less favourable with increasing temperature. Due to a mutual compensation, Gibbs free energy remains virtually unchanged. The strong negative DeltaC(p) value cannot solely be explained by the removal of hydrophobic surface portions of the protein or ligand from water exposure. Additional contributions must be considered, presumably arising from modulations of the local water structure, changes in vibrational modes or other ordering parameters. For thrombin, smaller negative DeltaC(p) values are observed for ligand binding in the presence of sodium ions compared to the other alkali ions, probably due to stabilising effects on the protein or changes in the bound water structure.

A new target for shigellosis: rational design and crystallographic studies of inhibitors of tRNA-guanine transglycosylase.

Eubacterial tRNA-guanine transglycosylase (TGT) is involved in the hyper-modification of cognate tRNAs leading to the exchange of G34 at the wobble position in the anticodon loop by preQ1 (2-amino-5-(aminomethyl)pyrrolo[2,3-d]pyrimidin-4(3H)-one) as part of the biosynthesis of queuine (Q). Mutation of the tgt gene in Shigella flexneri results in a significant loss of pathogenicity of the bacterium, revealing TGT as a new target for the design of potent drugs against Shigellosis. The X-ray structure of Zymomonas mobilis TGT in complex with preQ1 was used to search for new putative inhibitors with the computer program LUDI. An initial screen of the Available Chemical Directory, a database compiled from commercially available compounds, suggested several hits. Of these, 4-aminophthalhydrazide (APH) showed an inhibition constant in the low micromolar range. The 1.95 A crystal structure of APH in complex with Z. mobilis TGT served as a starting point for further modification of this initial lead.

Recent developments in structure-based drug design.

Structure-based design has emerged as a new tool in medicinal chemistry. A prerequisite for this new approach is an understanding of the principles of molecular recognition in protein-ligand complexes. If the three-dimensional structure of a given protein is known, this information can be directly exploited for the retrieval and design of new ligands. Structure-based ligand design is an iterative approach. First of all, it requires the crystal structure or a model derived from the crystal structure of a closely related homolog of the target protein, preferentially complexed with a ligand. This complex unravels the binding mode and conformation of a ligand under investigation and indicates the essential aspects determining its binding affinity. It is then used to generate new ideas about ways of improving an existing ligand or of developing new alternative bonding skeletons. Computational methods supplemented by molecular graphics are applied to assist this step of hypothesis generation. The features of the protein binding pocket can be translated into queries used for virtual computer screening of large compound libraries or to design novel ligands de novo. These initial proposals must be confirmed experimentally. Subsequently they are optimized toward higher affinity and better selectivity. The latter aspect is of utmost importance in defining and controlling the pharmacological profile of a ligand. A prerequisite to tailoring selectivity by rational design is a detailed understanding of molecular parameters determining selectivity. Taking examples from current drug development programs (HIV proteinase, t-RNA transglycosylase, thymidylate synthase, thrombin and, related serine proteinases), we describe recent advances in lead discovery via computer screening, iterative design, and understanding of selectivity discrimination.

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.

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.

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.

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.

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.

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.

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.

L-dopa potentiating analogs of Pro-Leu-Gly-NH2 with oral efficacy.

Chemical modification of PLG, comprising replacement of proline or/and leucine by unnatural amino acids led to analogs with high oral efficacy. The most active analog identified in the course of our works was 1-prolyl-2-phenyl-1-2-aminobutanoylglycinamide (Doreptide). Doreptide is presently further evaluated as a new drug for the treatment of Parkinson's disease.

Impact of ligand and protein desolvation on ligand binding to the S1 pocket of thrombin.

In the present study, we investigate the impact of a tightly bound water molecule on ligand binding in the S1 pocket of thrombin. The S1 pocket contains a deeply buried deprotonated aspartate residue (Asp189) that is, due to its charged state, well hydrated in the uncomplexed state. We systematically studied the importance of this water molecule by evaluating a series of ligands that contains pyridine-type P1 side chains that could potentially alter the binding properties of this water molecule. All of the pyridine derivatives retain the original hydration state albeit sometimes with a slight perturbance. In order to prevent a direct H-bond formation with Asp189, and to create a permanent positive charge on the P1 side chain that is positioned adjacent to the Asp189 carboxylate anion, we methylated the pyridine nitrogen. This methylation resulted in displacement of water but was accompanied by a loss in binding affinity. Quantum chemical calculations of the ligand solvation free energy showed that the positively charged methylpyridinium derivatives suffer a large penalty of desolvation upon binding. Consequently, they have a substantially less favorable enthalpy of binding. In addition to the ligand desolvation penalty, the hydration shell around Asp189 has to be overcome, which is achieved in nearly all pyridinium derivatives. Only for the ortho derivative is a partial population of a water next to Asp189 found. Possibly, the gain of electrostatic interactions between the charged P1 side chain and Asp189 helps to compensate for the desolvation penalty. In all uncharged pyridine derivatives, the solvation shell remains next to Asp189, partly mediating interactions between ligand and protein. In the case of the para-pyridine derivative, a strongly disordered cluster of water sites is observed between ligand and Asp189.