Allosteric targeting resolves limitations of earlier LFA-1 directed modalities.

Integrins are a family of cell surface receptors well-recognized for their therapeutic potential in a wide range of diseases. However, the development of integrin targeting medications has been impacted by unexpected downstream effects, reflecting originally unforeseen interference with the bidirectional signalling and cross-communication of integrins. We here selected one of the most severely affected target integrins, the integrin lymphocyte function-associated antigen-1 (LFA-1, αLß2, CD11a/CD18), as a prototypic integrin to systematically assess and overcome these known shortcomings. We employed a two-tiered ligand-based virtual screening approach to identify a novel class of allosteric small molecule inhibitors targeting this integrin's αI domain. The newly discovered chemical scaffold was derivatized, yielding potent bis-and tris-aryl-bicyclic-succinimides which inhibit LFA-1 in vitro at low nanomolar concentrations. The characterisation of these compounds in comparison to earlier LFA-1 targeting modalities established that the allosteric LFA-1 inhibitors (i) are devoid of partial agonism, (ii) selectively bind LFA-1 versus other integrins, (iii) do not trigger internalization of LFA-1 itself or other integrins and (iv) display oral availability. This profile differentiates the new generation of allosteric LFA-1 inhibitors from previous ligand mimetic-based LFA-1 inhibitors and anti-LFA-1 antibodies, and is projected to support novel immune regulatory regimens selectively targeting the integrin LFA-1. The rigorous computational and experimental assessment schedule described here is designed to be adaptable to the preclinical discovery and development of novel allosterically acting compounds targeting integrins other than LFA-1, providing an exemplary approach for the early characterisation of next generation integrin inhibitors.

Linking phenotypes and modes of action through high-content screen fingerprints.

High-content screening (HCS) is a powerful technique for monitoring phenotypic responses to treatments on a cellular and subcellular level. Cellular phenotypes can be characterized by multivariate image readouts such as shape, intensity, or texture. The corresponding feature vectors can thus be defined as HCS fingerprints that serve as a powerful biological compound descriptor. Therefore, clustering or classification of HCS fingerprints across compound treatments allows for the identification of similarities in protein targets or pathways. We developed an HCS-based profiling panel that serves as basis for characterizing the mode of action of compounds. This panel measures phenotypic effects in six different compartments of U-2OS cells, namely the nucleus, the cytoplasm, the endoplasmic reticulum, the Golgi apparatus, and the cytoskeleton. We profiled a set of 2,725 well-annotated compounds and clustered their corresponding HCS fingerprints to establish links between predominant cellular phenotypes and cellular processes and protein targets. We found various different clusters enriched for individual targets (e.g., HDAC, HSP90, TOP1, HMGCR, TUB), signaling pathways (e.g., PIK3/AKT/mTOR), or gene sets associated with diseases (e.g., psoriasis, leukemia). Based on this clustering we were able to identify novel compound-target associations for selected compounds such as a submicromolar inhibitory activity of Silmitasertib (a casein kinase inhibitor) on PI3K and mTOR.

In Silico Adoption of an Orphan Nuclear Receptor NR4A1.

A 4.1 µs molecular dynamics simulation of the NR4A1 (hNur77) apo-protein has been undertaken and a previously undetected druggable pocket has become apparent that is located remotely from the 'traditional' nuclear receptor ligand-binding site. A NR4A1/bis-indole ligand complex at this novel site has been found to be stable over 1 µs of simulation and to result in an interesting conformational transmission to a remote loop that has the capacity to communicate with a NBRE within a RXR-α/NR4A1 heterodimer. Several features of the simulations undertaken indicate how NR4A1 can be affected by alternate-site modulators.

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.

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.

Benchmarking of multivariate similarity measures for high-content screening fingerprints in phenotypic drug discovery.

High-content screening (HCS) is a powerful tool for drug discovery being capable of measuring cellular responses to chemical disturbance in a high-throughput manner. HCS provides an image-based readout of cellular phenotypes, including features such as shape, intensity, or texture in a highly multiplexed and quantitative manner. The corresponding feature vectors can be used to characterize phenotypes and are thus defined as HCS fingerprints. Systematic analyses of HCS fingerprints allow for objective computational comparisons of cellular responses. Such comparisons therefore facilitate the detection of different compounds with different phenotypic outcomes from high-throughput HCS campaigns. Feature selection methods and similarity measures, as a basis for phenotype identification and clustering, are critical for the quality of such computational analyses. We systematically evaluated 16 different similarity measures in combination with linear and nonlinear feature selection methods for their potential to capture biologically relevant image features. Nonlinear correlation-based similarity measures such as Kendall's τ and Spearman's ρ perform well in most evaluation scenarios, outperforming other frequently used metrics (such as the Euclidian distance). We also present four novel modifications of the connectivity map similarity that surpass the original version, in our experiments. This study provides a basis for generic phenotypic analysis in future HCS campaigns.

Phenotype-based high-content chemical library screening identifies statins as inhibitors of in vivo lymphangiogenesis.

Lymphangiogenesis plays an important role in promoting cancer metastasis to sentinel lymph nodes and beyond and also promotes organ transplant rejection. We used human lymphatic endothelial cells to establish a reliable three-dimensional lymphangiogenic sprouting assay with automated image acquisition and analysis for inhibitor screening. This high-content phenotype-based assay quantifies sprouts by automated fluorescence microscopy and newly developed analysis software. We identified signaling pathways involved in lymphangiogenic sprouting by screening the Library of Pharmacologically Active Compounds (LOPAC)(1280) collection of pharmacologically relevant compounds. Hit characterization revealed that mitogen-activated protein kinase kinase (MEK) 1/2 inhibitors substantially block lymphangiogenesis in vitro and in vivo. Importantly, the drug class of statins, for the first time, emerged as potent inhibitors of lymphangiogenic sprouting in vitro and of corneal and cutaneous lymphangiogenesis in vivo. This effect was mediated by inhibition of the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase and subsequently the isoprenylation of Rac1. Supplementation with the enzymatic products of HMG-CoA reductase functionally rescued lymphangiogenic sprouting and the recruitment of Rac1 to the plasma membrane.

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.

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.

DOGS: reaction-driven de novo design of bioactive compounds.

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated 'in silico' assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.

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 and biological evaluation of a novel class of dual microsomal prostaglandin E2 synthase-1/5-lipoxygenase inhibitors based on 2-[(4,6-diphenethoxypyrimidin-2-yl)thio]hexanoic acid.

Various inflammatory diseases are associated with the excessive formation of leukotrienes (LTs) and prostaglandins (PGs). Herein, we present a novel class of dual inhibitors of 5-lipoxygenase (5-LO) and microsomal prostaglandin E(2) synthase-1 (mPGES-1), key enzymes in the formation of LTs and PGE(2), respectively. On the basis of the structure of 2-[(4,6-diphenethoxypyrimidin-2-yl)thio]hexanoic acid (1), we performed a detailed SAR analysis, and mechanistic studies were carried out to elucidate the mode of 5-LO inhibition. Interestingly, the pyrimidine ring including the thioether of 1 could be replaced by a simple benzyl or a benzylidene moiety yielding a novel series of bioactive 2-benzylidene- and 2-benzylhexanoic acids exemplified by 2-(2,3-diphenethoxybenzylidene)hexanoic acid, 29 (IC(50) 5-LO = 0.8 µM; mPGES-1 = 1.1 µM). Importantly, none of the novel bioactive derivatives strongly inhibited cyclooxygenase activities. Together, we provide novel promising lead compounds for the treatment of inflammatory diseases valuable for further investigations in vivo.

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.

Self-organizing fuzzy graphs for structure-based comparison of protein pockets.

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.

Reaction-MQL: line notation for functional transformation.

Representation of chemical reactions is pivotal for different purposes in cheminformatics. We present an extension of the molecular query language (MQL), which combines readable style with meaningful rules for string representation of reactions and unambiguous product formation. The concept of functional groups is used to describe the transformations. Functional groups are defined in terms of substructure queries and are processed by graph transformations. Molecular educt graphs are transformed by application of beginning-, end-, and reaction-matrices to obtain the product graph without consideration of stereochemistry. Both directions of a transformation are possible. We implemented the concept of Reaction-MQL in Java employing the Chemistry Development Kit.