Fuzzy matched pairs: a means to determine the pharmacophore impact on molecular interaction.

Within this work, a methodological extension of the matched molecular pair analysis is presented. The method is based on a pharmacophore retyping of the molecular graph and a consecutive matched molecular pair analysis. The features of the new methodology are exemplified using a large data set on CYP inhibition. We show that Fuzzy Matched Pairs can be used to extract activity and selectivity determining pharmacophoric features. Based on the fuzzy pharmacophore description, the method clusters molecular transfers and offers new opportunities for the combination of data from different sources, namely public and industry datasets.

Targeting dynamic pockets of HIV-1 protease by structure-based computational screening for allosteric inhibitors.

We present the discovery of low molecular weight inhibitors of human immunodeficiency virus 1 (HIV-1) protease subtype B that were identified by structure-based virtual screening as ligands of an allosteric surface cavity. For pocket identification and prioritization, we performed a molecular dynamics simulation and observed several flexible, partially transient surface cavities. For one of these presumable ligand-binding pockets that are located in the so-called "hinge region" of the identical protease chains, we computed a receptor-derived pharmacophore model, with which we retrieved fragment-like inhibitors from a screening compound pool. The most potent hit inhibited protease activity in vitro in a noncompetitive mode of action. Although attempts failed to crystallize this ligand bound to the enzyme, the study provides proof-of-concept for identifying innovative tool compounds for chemical biology by addressing flexible protein models with receptor pocket-derived pharmacophore screening.

Biological networks and drug discovery--where do we stand?

The progress in genomics and molecular biology during the last decade has led to the insight that complex diseases are caused by defects in many genes and that cellular processes are organized in complex networks. In the search of new drugs, these findings have not yet been fully taken into account. The currently predominating drug discovery approach in pharmaceutical industry is still target-centric, with the goal to optimize compounds toward high affinity and selectivity for a specific target. There is growing evidence that the successful development of effective disease-modifying medications needs a network approach instead. This might also help to reduce the very high failure rate in the current drug development process. This review outlines concepts and applications of network biology and routes toward drugs that are designed to interact with several targets. Obstacles to the implementation of this approach in the pharmaceutical industry are discussed.

Distinct roles of secreted HtrA proteases from gram-negative pathogens in cleaving the junctional protein and tumor suppressor E-cadherin.

The periplasmic chaperone and serine protease HtrA is important for bacterial stress responses and protein quality control. Recently, we discovered that HtrA from Helicobacter pylori is secreted and cleaves E-cadherin to disrupt the epithelial barrier, but it remained unknown whether this maybe a general virulence mechanism. Here, we show that important other pathogens including enteropathogenic Escherichia coli, Shigella flexneri, and Campylobacter jejuni, but not Neisseria gonorrhoeae, cleaved E-cadherin on host cells. HtrA deletion in C. jejuni led to severe defects in E-cadherin cleavage, loss of cell adherence, paracellular transmigration, and basolateral invasion. Computational modeling of HtrAs revealed a conserved pocket in the active center exhibiting pronounced proteolytic activity. Differential E-cadherin cleavage was determined by an alanine-to-glutamine exchange in the active center of neisserial HtrA. These data suggest that HtrA-mediated E-cadherin cleavage is a prevalent pathogenic mechanism of multiple gram-negative bacteria representing an attractive novel target for therapeutic intervention to combat bacterial infections.

Identification of UV-protective activators of nuclear factor erythroid-derived 2-related factor 2 (Nrf2) by combining a chemical library screen with computer-based virtual screening.

Nuclear factor erythroid-derived 2-related factor 2 (Nrf2) is a master regulator of cellular antioxidant defense systems, and activation of this transcription factor is a promising strategy for protection of skin and other organs from environmental insults. To identify efficient Nrf2 activators in keratinocytes, we combined a chemical library screen with computer-based virtual screening. Among 14 novel Nrf2 activators, the most potent compound, a nitrophenyl derivative of 2-chloro-5-nitro-N-phenyl-benzamide, was characterized with regard to its molecular mechanism of action. This compound induced the expression of cytoprotective genes in keratinocytes isolated from wild-type but not from Nrf2-deficient mice. Most importantly, it showed low toxicity and protected primary human keratinocytes from UVB-induced cell death. Therefore, it represents a potential lead compound for the development of drugs for skin protection under stress conditions. Our study demonstrates that chemical library screening combined with advanced computational similarity searching is a powerful strategy for identification of bioactive compounds, and it points toward an innovative therapeutic approach against UVB-induced skin damage.

Virtual screening for compounds that mimic protein-protein interface epitopes.

Modulation of protein-protein interactions (PPI) has emerged as a new concept in rational drug design. Here, we present a computational protocol for identifying potential PPI inhibitors. Relevant regions of interfaces (epitopes) are predicted for three-dimensional protein models and serve as queries for virtual compound screening. We present a computational screening protocol that incorporates two different pharmacophore models. One model is based on the mathematical concept of autocorrelation vectors and the other utilizes fuzzy labeled graphs. In a proof-of-concept study, we were able to identify serine protease inhibitors using a predicted trypsin epitope as query. Our virtual screening framework may be suited for rapid identification of PPI inhibitors and suggesting bioactive tool compounds.

Discovery of small-molecule interleukin-2 inhibitors from a DNA-encoded chemical library.

Libraries of chemical compounds individually coupled to encoding DNA tags (DNA-encoded chemical libraries) hold promise to facilitate exceptionally efficient ligand discovery. We constructed a high-quality DNA-encoded chemical library comprising 30,000 drug-like compounds; this was screened in 170 different affinity capture experiments. High-throughput sequencing allowed the evaluation of 120 million DNA codes for a systematic analysis of selection strategies and statistically robust identification of binding molecules. Selections performed against the tumor-associated antigen carbonic anhydrase IX (CA IX) and the pro-inflammatory cytokine interleukin-2 (IL-2) yielded potent inhibitors with exquisite target specificity. The binding mode of the revealed pharmacophore against IL-2 was confirmed by molecular docking. Our findings suggest that DNA-encoded chemical libraries allow the facile identification of drug-like ligands principally to any protein of choice, including molecules capable of disrupting high-affinity protein-protein interactions.

Helicobacter pylori HtrA is a new secreted virulence factor that cleaves E-cadherin to disrupt intercellular adhesion.

Mammalian and prokaryotic high-temperature requirement A (HtrA) proteins are chaperones and serine proteases with important roles in protein quality control. Here, we describe an entirely new function of HtrA and identify it as a new secreted virulence factor from Helicobacter pylori, which cleaves the ectodomain of the cell-adhesion protein E-cadherin. E-cadherin shedding disrupts epithelial barrier functions allowing H. pylori designed to access the intercellular space. We then designed a small-molecule inhibitor that efficiently blocks HtrA activity, E-cadherin cleavage and intercellular entry of H. pylori.

Protein-protein docking by shape-complementarity and property matching.

We present a computational approach to protein-protein docking based on surface shape complementarity ("ProBinder"). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as "knobs," "holes," and "flats" together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation-, and rotation-free comparison of point coordinates. Candidate solutions were scored based on knowledge-based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van-der-Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty-four percent of the protein-protein test complexes were ranked with an root mean square deviation (RMSD) < 5 A to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 A to the target solution.

Unraveling the Activation Mechanism of Taspase1 which Controls the Oncogenic AF4-MLL Fusion Protein.

We have recently demonstrated that Taspase1-mediated cleavage of the AF4-MLL oncoprotein results in the formation of a stable multiprotein complex which forms the key event for the onset of acute proB leukemia in mice. Therefore, Taspase1 represents a conditional oncoprotein in the context of t(4;11) leukemia. In this report, we used site-directed mutagenesis to unravel the molecular events by which Taspase1 becomes sequentially activated. Monomeric pro-enzymes form dimers which are autocatalytically processed into the enzymatically active form of Taspase1 (αßßα). The active enzyme cleaves only very few target proteins, e.g., MLL, MLL4 and TFIIA at their corresponding consensus cleavage sites (CSTasp1) as well as AF4-MLL in the case of leukemogenic translocation. This knowledge was translated into the design of a dominant-negative mutant of Taspase1 (dnTASP1). As expected, simultaneous expression of the leukemogenic AF4-MLL and dnTASP1 causes the disappearance of the leukemogenic oncoprotein, because the uncleaved AF4-MLL protein (328 kDa) is subject to proteasomal degradation, while the cleaved AF4-MLL forms a stable oncogenic multi-protein complex with a very long half-life. Moreover, coexpression of dnTASP1 with a BFP-CSTasp1-GFP FRET biosensor effectively inhibits cleavage. The impact of our findings on future drug development and potential treatment options for t(4;11) leukemia will be discussed.

Industrial applications of in silico ADMET.

Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.

Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site.

Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative "homology" model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.

Context-based identification of protein-protein interfaces and "hot-spot" residues.

Reliable determination of protein-protein interaction sites is of critical importance for structure-based design of small molecules modulating protein function through macromolecular interfaces. We present an alignment-free computational method for prediction of protein-protein interface residues. The method ("iPred") is based on a knowledge-based scoring function adapted from the field of protein folding and small molecule docking. Based on a training set of 394 hetero-dimeric proteins iPred achieves sustained accuracy on an external unbound test set. Prediction robustness was assessed from more than 1500 diverse complexes containing homo- and hetero-dimers. The technique does not rely on sequence conservation, so that rapid interface identification is possible even for proteins for which homologs are unknown or lack conserved residue patterns in interface region. Functional "hot-spot" residues are enriched among the predicted interface residues, rendering the method predestined for macromolecular binding site identification and drug design studies aiming at modulating protein-protein interaction that might influence protein function. For a comparative structural model of peptidase HtrA from Helicobacter pylori, we performed mutation studies for predicted hot-spot residues, which were confirmed as functionally relevant for HtrA activity or oligomerization.

Inhibitors of Helicobacter pylori protease HtrA found by 'virtual ligand' screening combat bacterial invasion of epithelia.

BACKGROUND: The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. METHODOLOGY/PRINCIPAL FINDINGS: We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector ('virtual ligand') for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that receptor-based virtual screening with a permissive ('fuzzy') pharmacophore model can help identify small bioactive agents for combating bacterial infection.

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors.

BACKGROUND: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. METHODOLOGY/PRINCIPAL FINDINGS: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. CONCLUSIONS/SIGNIFICANCE: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors.

BACKGROUND: De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis. METHODS: We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro. RESULTS: The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested. CONCLUSIONS: Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

Structural properties of so-called NSAID-phospholipid-complexes.

So-called NSAID-phospholipid-complexes have been recently reported in literature to reduce local gastrointestinal toxicity. The present work was dedicated to the structural characterization of so-called drug-phospholipid-complexes on the example of diclofenac sodium, ibuprofen and piroxicam complexes with dipalmitoylphosphatidylcholine (DPPC) at different stages of preparation. The applied techniques include (1)H/2D ROESY NMR for the structural characterization in organic solvents, FT-IR and X-ray diffraction for the structural characterization in the solid state and PCS, (31)P NMR, as well as MAS (1)H/2D NOESY NMR for the structural characterization in aqueous media following hydration. Whereas the formation of isolated 1:1 drug-phospholipid-complexes with a preferential location of diclofenac and ibuprofen at the polar head group, stabilized by cation-π interaction, seems reasonable in organic solvents, it was found that mainly liposomal and micellar structures are formed upon hydration of the drug-phospholipid-complexes. Hence the term "NSAID-phospholipid-complex" may be misleading in the context with physiologically relevant aqueous media. Piroxicam did not show significant interaction with DPPC.

Homology model adjustment and ligand screening with a pseudoreceptor of the human histamine H4 receptor.

A computer-assisted method for the generation of pseudoreceptor models is presented together with two practical applications. From a three-dimensional alignment of known histamine H(4) receptor ligands, a pseudoreceptor model of the putative ligand binding site was constructed and used for virtual screening of a large collection of commercially available compounds. Two bioactive chemotypes were retrieved, demonstrating the general applicability of the approach. The pseudoreceptor model was also used to find the putative ligand binding pocket within the transmembrane receptor domain. For each frame of a molecular dynamics simulation of a homology-based H(4) receptor model, we automatically extracted potential ligand binding pockets and used their compatibility with the pseudoreceptor as a selection criterion. The best-matching pocket fits perfectly with existing mutation data and previously published hypotheses suggesting Glu182(5.46) as the preferred binding partner of a positively charged moiety of H(4) receptor ligands. This new pseudoreceptor approach has demonstrated its suitability for both structure-based prioritization of protein receptor models, and ligand-based virtual screening with the aim to perform scaffold hopping.