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DOGS: reaction-driven de novo design of bioactive compounds (2012)

Markus Hartenfeller Heiko Zettl Miriam Walter Matthias Rupp Felix Reisen Ewgenij Proschak Sascha Weggen Holger Stark Gisbert Schneider

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.

Bioassays to monitor taspase1 function for the identification of pharmacogenetic inhibitors (2011)

Shirley Knauer Verena Fetz Jens Rabenstein Sandra Friedl Bettina Hofmann Samaneh Sabiani Elisabeth Schröder Lena Kunst Eugen Proschak Eckhard Thines Thomas Kindler Gisbert Schneider Rolf Marschalek Roland Stauber Carolin Bier

Background: Threonine Aspartase 1 (Taspase1) mediates cleavage of the mixed lineage leukemia (MLL) protein and leukemia provoking MLL-fusions. In contrast to other proteases, the understanding of Taspase1's (patho)biological relevance and function is limited, since neither small molecule inhibitors nor cell based functional assays for Taspase1 are currently available. Methodology/Findings: Efficient cell-based assays to probe Taspase1 function in vivo are presented here. These are composed of glutathione S-transferase, autofluorescent protein variants, Taspase1 cleavage sites and rational combinations of nuclear import and export signals. The biosensors localize predominantly to the cytoplasm, whereas expression of biologically active Taspase1 but not of inactive Taspase1 mutants or of the protease Caspase3 triggers their proteolytic cleavage and nuclear accumulation. Compared to in vitro assays using recombinant components the in vivo assay was highly efficient. Employing an optimized nuclear translocation algorithm, the triple-color assay could be adapted to a high-throughput microscopy platform (Z'factor = 0.63). Automated high-content data analysis was used to screen a focused compound library, selected by an in silico pharmacophor screening approach, as well as a collection of fungal extracts. Screening identified two compounds, N-[2-[(4-amino-6-oxo-3H-pyrimidin-2-yl)sulfanyl]ethyl]benzenesulfonamideand 2-benzyltriazole-4,5-dicarboxylic acid, which partially inhibited Taspase1 cleavage in living cells. Additionally, the assay was exploited to probe endogenous Taspase1 in solid tumor cell models and to identify an improved consensus sequence for efficient Taspase1 cleavage. This allowed the in silico identification of novel putative Taspase1 targets. Those include the FERM Domain-Containing Protein 4B, the Tyrosine-Protein Phosphatase Zeta, and DNA Polymerase Zeta. Cleavage site recognition and proteolytic processing of these substrates were verified in the context of the biosensor. Conclusions: The assay not only allows to genetically probe Taspase1 structure function in vivo, but is also applicable for high-content screening to identify Taspase1 inhibitors. Such tools will provide novel insights into Taspase1's function and its potential therapeutic relevance.

Inhibitors of Helicobacter pylori protease HtrA found by "virtual ligand" screening combat bacterial invasion of epithelia (2011)

Martin Löwer Tim Geppert Petra Schneider Benjamin Hoy Silja Weßler Gisbert Schneider

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.

Unterwegs in chemischen Räumen : Chemieinformatik und Moleküldesign (2003)

Gisbert Schneider

Wie findet man einen neuen Wirkstoff? Die pharmazeutisch-chemische Forschung steht mit diesem Vorhaben vor einer scheinbar unlösbaren Aufgabe, denn der "chemische Raum" aller wirkstoffartigen Moleküle ist unvorstellbar groß. So wurde geschätzt, dass man prinzipiell aus 1060 bis 10100 verschiedenen Verbindungen die geeigneten Kandidaten auswählen kann. Zum Vergleich: Seit dem Urknall sollen "nur" etwa 10 hoch 18 Sekunden, etwa 14 Milliarden Jahre, vergangen sein. Dies bedeutet, dass der chemische Raum praktisch unendlich ist. Aus dieser Überlegung lassen sich zumindest zwei Schlussfolgerungen ziehen: Zum einen gibt es die begründete Hoffnung, dass ein Molekül mit der gewünschten Aktivität existiert, zum anderen stellt sich die Frage, wie diese unvorstellbar große Zahl chemischer Verbindungen systematisch durchmustert werden kann? Doch die Situation ist nicht so hoffnungslos, wie sie auf den ersten Blick erscheint. Dies zeigt die erfolgreiche Entwicklung immer neuer Medikamente. Das Forschungsgebiet der Chemieinformatik befasst sich mit der Entwicklung von intelligenten Lösungsansätzen, die Chemikern bei dieser Suche nach den "Nadeln im riesigen Heuhaufen" helfen können.

MHC I stabilizing potential of computer-designed octapeptides (2010)

Joanna M. Wisniewska Natalie Jäger Anja Freier Florian O. Losch Karl-Heinz Wiesmüller Peter Walden Paul Wrede Gisbert Schneider Jan A. Hiss

Experimental results are presented for 180 in silico designed octapeptide sequences and their stabilizing effects on the major histocompatibility class I molecule H-2Kb. Peptide sequence design was accomplished by a combination of an ant colony optimization algorithm with artificial neural network classifiers. Experimental tests yielded nine H-2Kb stabilizing and 171 nonstabilizing peptides. 28 among the nonstabilizing octapeptides contain canonical motif residues known to be favorable for MHC I stabilization. For characterization of the area covered by stabilizing and non-stabilizing octapeptides in sequence space, we visualized the distribution of 100,603 octapeptides using a self-organizing map. The experimental results present evidence that the canonical sequence motives of the SYFPEITHI database on their own are insufficient for predicting MHC I protein stabilization.

Kernel learning for ligand-based virtual screening:discovery of a new PPARgamma agonist (2010)

Matthias Rupp Timon Schroeter Ramona Steri Ewgenij Proschak Katja Hansen Heiko Zettl Oliver Rau Manfred Schubert-Zsilavecz Klaus-Robert Müller Gisbert Schneider

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.

SBE13, a newly identified inhibitor of inactive polo-like kinase 1 (2010)

Sarah Keppner Ewgenij Proschak Gisbert Schneider Birgit Spänkuch

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 Protein kinases are important targets for drug development. The almost identical protein folding of kinases and the common co-substrate ATP leads to the problem of inhibitor selectivity. Type II inhibitors, targeting the inactive conformation of kinases, occupy a hydrophobic pocket with less conserved surrounding amino acids [1]. Human polo-like kinase 1 (Plk1) represents a promising target for approaches to identify new therapeutic agents. Plk1 belongs to a family of highly conserved serine/threonine kinases, and is a key player in mitosis, where it modulates the spindle checkpoint at metaphase/anaphase transition. Plk1 is over-expressed in all today analyzed human tumors of different origin and serves as a negative prognostic marker in cancer patients. The newly identified inhibitor, SBE13, a vanillin derivative, targets Plk1 in its inactive conformation [2]. This leads to selectivity within the Plk family and towards Aurora A. This selectivity can be explained by docking studies of SBE13 into the binding pocket of homology models of Plk1, Plk2 and Plk3 in their inactive conformation. SBE13 showed anti-proliferative effects in cancer cell lines of different origins with EC50 values between 5 microM and 39 microM and induced apoptosis. Increasing concentrations of SBE13 result in increasing amounts of cells in G2/M phase 13 hours after double thymidin block of HeLa cells. The kinase activity of Plk1 was inhibited with an IC50 of 200 pM. Taken together, we could show that carefully designed structure-based virtual screening is well-suited to identify selective type II kinase inhibitors targeting Plk1 as potential anti-cancer therapeutics.

Fuzzy virtual ligands for virtual screening (2009)

Martin Löwer Yusuf Tanrikulu Martin Weisel Gisbert Schneider

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers.

Identification of Plk1 type II inhibitors by structure-based virtual screening (2009)

Sarah Keppner Ewgenij Proschak Gisbert Schneider Birgit Spänkuch

Protein kinases are targets for drug development [1]. Dysregulation of kinase activity leads to various diseases [2], e.g. cancer, inflammation, diabetes [1]. Human polo-like kinase 1 (Plk1), a serine/threonine kinase, is a cancer-relevant gene and a potential drug target which attracts increasing attention in the field of cancer therapy. Plk1 is a key player in mitosis and modulates entry into mitosis and the spindle checkpoint at the meta-/anaphase transition. Plk1 overexpression is observed in various human tumors, and it is a negative prognostic factor for cancer patients [3]. The same catalytical mechanism and the same co-substrate (ATP) lead to the problem of inhibitor selectivity. A strategy to solve this problem is represented by targeting the inactive conformation of kinases [2]. Kinases undergo conformational changes between active and inactive conformation and thus an additional hydrophobic pocket is created in the inactive conformation where the surrounding amino acids are less conserved [2]. A "homology model" of the inactive conformation of Plk1 was constructed, as the crystal structure in its inactive conformation is unknown. A crystal structure of Aurora A kinase served as template structure. With this homology model a receptor-based pharmacophore search was performed using SYBYL7.3 software. The raw hits were filtered using physico-chemical properties. The resulting hits were docked using Gold3.2 software, and 13 candidates for biological testing were manually selected. Three compounds of the 13 tested exhibit anti-proliferative effects in HeLa cancer cells. The most potent inhibitor, SBE13, was further tested in various other cancer cell lines of different origins and displayed EC50 values between 12 microM and 39 microM. Cancer cells incubated with SBE13 showed induction of apoptosis, detected by PARP (Poly-Adenosyl-Ribose-Polymerase) cleavage, caspase 9 activation and DAPI staining of apoptotic nuclei.

Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and gaussian processes (2009)

Timon Schroeter Matthias Rupp Katja Hansen Klaus-Robert Müller Gisbert Schneider

For a virtual screening study, we introduce a combination of machine learning techniques, employing a graph kernel, Gaussian process regression and clustered cross-validation. The aim was to find ligands of peroxisome-proliferator activated receptor gamma (PPAR-y). The receptors in the PPAR family belong to the steroid-thyroid-retinoid superfamily of nuclear receptors and act as transcription factors. They play a role in the regulation of lipid and glucose metabolism in vertebrates and are linked to various human processes and diseases [1]. For this study, we used a dataset of 176 PPAR-y agonists published by Ruecker et al [2]. Gaussian process (GP) models can provide a confidence estimate for each individual prediction, thereby allowing to assess which compounds are inside of the model's domain of applicability. This feature is useful in virtual screening, where a large fraction of the tested compounds may be outside of the model's domain of applicability. In cheminformatics, GPs have been applied to different classification and regression tasks using either radial basis function or rational quadratic kernels based on vectorial descriptors [4,5]. We used a graph kernel based on iterative similarity and optimal assignments (ISOAK, [3]) for non-linear Bayesian regression with Gaussian process priors (GP regression, [4]). A number of kernel-based learning algorithms (including GPs) are capable of multiple kernel learning [5], which allows combining heterogeneous information by using multiple kernels at the same time. In this work, we combined rational quadratic kernels for vectorial molecular descriptors (MOE2D, CATS2D and Ghose-Crippen fragment descriptors) with the ISOAK graph kernel. We evaluated our methodology in different ranking and regression settings. Ranking performance was assessed using the number of false positives within the top k predicted compounds. Predicted compounds were ranked based on both predicted binding affinity and the confidence in each prediction. In the regression setting, we employed standard loss functions like mean absolute error (MEA) and root mean squared error. The established linear ridge regression (LRR) and support vector regression (SVR) algorithms served as baseline methods. In addition to standard test/training splits and cross-validation, we used a clustered cross-validation strategy where clusters of compounds are left out when constructing training sets. This results in less optimistic results, but has the advantage of favouring more robust and potentially extrapolation-capable algorithms than standard training/test splits and normal cross-validation. In the regression setting, both GP and SVR models performed well, yielding MAEs as low as 0.66 +- 0.08 log units (clustered CV) and 0.51 +- 0.3 log units (normal CV). In the ranking setting, GPs slightly outperform SVR (0.21 +- 0.09 log units vs. 0.3 +- 0.08 log units). In conclusion, Gaussian process regression using simultaneously – via multiple kernel learning – the ISOAK molecular graph kernel and the rational quadratic kernel (with standard molecular descriptors) performs excellent in retrospective evaluation. A prospective evaluation study is currently in progress.

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