Latest documents http://publikationen.ub.uni-frankfurt.de/index/index/ Tue, 18 Dec 2012 12:47:32 +0100 Tue, 18 Dec 2012 12:47:32 +0100 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/27825 Background: Reconstitution of cytomegalovirus-specific CD3+CD8+ T cells (CMV-CTLs) after allogeneic hematopoietic stem cell transplantation (HSCT) is necessary to bring cytomegalovirus (CMV) reactivation under control. However, the parameters determining protective CMV-CTL reconstitution remain unclear to date. Design and Methods: In a prospective tri-center study, CMV-CTL reconstitution was analyzed in the peripheral blood from 278 patients during the year following HSCT using 7 commercially available tetrameric HLA-CMV epitope complexes. All patients included could be monitored with at least CMV-specific tetramer. Results: CMV-CTL reconstitution was detected in 198 patients (71%) after allogeneic HSCT. Most importantly, reconstitution with 1 CMV-CTL per µl blood between day +50 and day +75 post-HSCT discriminated between patients with and without CMV reactivation in the R+/D+ patient group, independent of the CMV-epitope recognized. In addition, CMV-CTLs expanded more daramtaically in patients experiencing only one CMV-reactivation than those without or those with multiple CMV reactivations. Monitoring using at least 2 tetramers was possible in 63% (n = 176) of the patients. The combinations of particular HLA molecules influenced the numbers of CMV-CTLs detected. The highest CMV-CTL count obtained for an individual tetramer also changed over time in 11% of these patients (n = 19) resulting in higher levels of HLA-B*0801 (IE-1) recognizing CMV-CTLs in 14 patients. Conclusions: Our results indicate that 1 CMV-CTL per µl blood between day +50 to +75 marks the beginning of an immune response against CMV in the R+/D+ group. Detection of CMV-CTL expansion thereafter indicates successful resolution of the CMV reactivation. Thus, sequential monitoring of CMV-CTL reconstitution can be used to predict patients at risk for recurrent CMV reactivation. Sylvia Borchers; Melanie Bremm; Thomas Lehrnbecher; Elke Dammann; Brigitte Pabst; Benno Wölk; Ruth Esser; Meral Yildiz; Matthias Eder; Michael Stadler; Peter Bader; Hans Martin; Andrea Jarisch; Gisbert Schneider; Thomas Klingebiel; Arnold Ganser; Eva Maria Mischak-Weissinger; Ulrike Köhl article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/27825 Tue, 18 Dec 2012 12:47:32 +0100 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/24617 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. Markus Hartenfeller; Heiko Zettl; Miriam Walter; Matthias Rupp; Felix Reisen; Ewgenij Proschak; Sascha Weggen; Holger Stark; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/24617 Mon, 02 Apr 2012 11:28:01 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22664 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. 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 article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22664 Thu, 08 Sep 2011 15:52:43 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22627 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. Quan Wang; Kerstin Birod; Carlo Federico Angioni; Sabine Grösch; Tim Geppert; Petra Schneider; Matthias Rupp; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22627 Wed, 07 Sep 2011 09:09:29 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22624 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. Martin Löwer; Tim Geppert; Petra Schneider; Benjamin Hoy; Silja Weßler; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22624 Tue, 06 Sep 2011 16:56:01 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/21813 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. Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/21813 Tue, 14 Jun 2011 09:21:59 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20013 This corrects the article "Prediction of Type III Secretion Signals in Genomes of Gram-Negative Bacteria" in PLoS ONE, e5917. urn:nbn:de:hebis:30-82663 A file was unintentionally omitted from the Supporting Information section of the published article: "Text S1. Training data." The file can be viewed here. Martin Löwer; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20013 Tue, 19 Oct 2010 16:34:49 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20012 Background: Pathogenic bacteria infecting both animals as well as plants use various mechanisms to transport virulence factors across their cell membranes and channel these proteins into the infected host cell. The type III secretion system represents such a mechanism. Proteins transported via this pathway (‘‘effector proteins’’) have to be distinguished from all other proteins that are not exported from the bacterial cell. Although a special targeting signal at the N-terminal end of effector proteins has been proposed in literature its exact characteristics remain unknown. Methodology/Principal Findings: In this study, we demonstrate that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques. Known protein effectors were compiled from the literature and sequence databases, and served as training data for artificial neural networks and support vector machine classifiers. Common sequence features were most pronounced in the first 30 amino acids of the effector sequences. Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%). Conclusions/Significance: We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes. Our study demonstrates that the analyzed signal features are common across a wide range of species, and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. The prediction software is publicly accessible from our web server ( www.modlab.org ). Martin Löwer; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20012 Tue, 19 Oct 2010 16:31:08 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20011 Bacterial autotransporters represent a diverse family of proteins that autonomously translocate across the inner membrane of Gram-negative bacteria via the Sec complex and across the outer bacterial membrane. They often possess exceptionally long N-terminal signal sequences. We analyzed 90 long signal sequences of bacterial autotransporters and members of the two-partner secretion pathway in silico and describe common domain organization found in 79 of these sequences. The domains are in agreement with previously published experimental data. Our algorithmic approach allows for the systematic identification of functionally different domains in long signal sequences. Keywords: bacterial autotransporter, sequence analysis, pattern, protein targeting, signal peptide, protein trafficking Jan Alexander Hiß; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/20011 Tue, 19 Oct 2010 16:13:51 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/8109 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. Joanna M. Wisniewska; Natalie Jäger; Anja Freier; Florian O. Losch; Karl-Heinz Wiesmüller; Peter Walden; Paul Wrede; Gisbert Schneider; Jan A. Hiss article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/8109 Thu, 23 Sep 2010 16:44:35 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/638 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. Matthias Rupp; Timon Schroeter; Ramona Steri; Ewgenij Proschak; Katja Hansen; Heiko Zettl; Oliver Rau; Manfred Schubert-Zsilavecz; Klaus-Robert Müller; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/638 Wed, 19 May 2010 11:32:52 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7767 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. Sarah Keppner; Ewgenij Proschak; Gisbert Schneider; Birgit Spänkuch article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7767 Wed, 19 May 2010 10:09:08 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6791 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. Martin Löwer; Yusuf Tanrikulu; Martin Weisel; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6791 Wed, 26 Aug 2009 14:17:22 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6790 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. Sarah Keppner; Ewgenij Proschak; Gisbert Schneider; Birgit Spänkuch article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6790 Wed, 26 Aug 2009 14:11:41 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6789 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. Timon Schroeter; Matthias Rupp; Katja Hansen; Klaus-Robert Müller; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6789 Wed, 26 Aug 2009 14:07:10 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6788 Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries. Felix Reisen; Markus Hartenfeller; Ewgenij Proschak; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6788 Wed, 26 Aug 2009 13:55:57 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6787 There is a renewed interest in pseudoreceptor models which enable computational chemists to bridge the gap of ligand- and receptor-based drug design [1]. We developed a pseudoreceptor model for the histamine H4 receptor (H4R) based on five potent antagonists representing different chemotypes. Here we present the selection of potential ligand binding pockets that occur during molecular dynamics (MD) simulations of a homology-based receptor model. We present a method for prioritizing receptor models according to their match with the consensus ligand-binding mode represented by the pseudoreceptor. In this way, ligand information can be transferred to receptor-based modelling. We use Geometric Hashing to match three-dimensional points in Cartesion space [2]. This allows for the rapid translation- and rotation-free comparison of atom coordinates, which also permits partial matching. The only prerequisite is a hash table, which uses distance triplets as hash keys. Each time a distance triplet occurring in the candidate point set which corresponds to an existing key, the match is represented by a vote of the respective key. Finally, the global match of both point sets can be easily extracted by selection of voted distance triplets. The results revealed a preferred ligand-binding pocket in H4R, which would not have been identified using an unrefined homology model of the protein. The key idea was to rely on ligand information by pseudoreceptor modelling. Tim Werner; Tim Geppert; Yusuf Tanrikulu; Ewgenij Proschak; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6787 Wed, 26 Aug 2009 13:51:49 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6786 We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled [1]. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment [2]. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D [3], MOE pharmacophore quadruples [4]) in retrospective virtual screenings using the COBRA [5] collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list. Volker Hähnke; Bettina Petra Hofmann; Ewgenij Proschak; Dieter Steinhilber; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6786 Wed, 26 Aug 2009 13:47:01 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6785 The representation of small molecules as molecular graphs [1] is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis. We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker [2] algorithm for characterization of binding site volumes and employs a Growing Neural Gas [3] procedure to derive graph representations of pocket topologies. Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes. Martin Weisel; Jan M. Kriegl; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6785 Wed, 26 Aug 2009 13:40:07 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6591 Shape complementarity is a compulsory condition for molecular recognition [1]. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment [2] with fuzzy pharmacophore scoring [3]. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified. SQUIRREL molecular superposition is based on a graph-matching routine [4] and allows partial matching. We used this advantage for searching for bioisosteric replacement suggestions in a database of molecular fragments derived from a collection of drug-like compounds [5]. The bioisosteric groups suggested by our tool SQURRELnovo, can be used for ligand-based de novo design by a human expert. Using the fibrate derivative GW590735 [6] as query, we designed a novel lead structure by substitution of the acidic head group and hydrophobic tail. The synthesis and following testing in a cell-based reporter gene assay [7,8] revealed that the designed structure activates PPARalpha with an EC50 of 510 nM. Ewgenij Proschak; Kerstin Sander; Heiko Zettl; Yusuf Tanrikulu; Petra Schneider; Oliver Rau; Holger Stark; Manfred Schubert-Zsilavecz; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6591 Tue, 09 Jun 2009 16:53:04 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6590 Matthias Rupp; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6590 Tue, 09 Jun 2009 16:45:20 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6012 Targeting signals direct proteins to their extra- or intracellular destination such as the plasma membrane or cellular organelles. Here we investigated the structure and function of exceptionally long signal peptides encompassing at least 40 amino acid residues. We discovered a two-domain organization ("NtraC model") in many long signals from vertebrate precursor proteins. Accordingly, long signal peptides may contain an N-terminal domain (N-domain) and a C-terminal domain (C-domain) with different signal or targeting capabilities, separable by a presumably turn-rich transition area (tra). Individual domain functions were probed by cellular targeting experiments with fusion proteins containing parts of the long signal peptide of human membrane protein shrew-1 and secreted alkaline phosphatase as a reporter protein. As predicted, the N-domain of the fusion protein alone was shown to act as a mitochondrial targeting signal, whereas the C-domain alone functions as an export signal. Selective disruption of the transition area in the signal peptide impairs the export efficiency of the reporter protein. Altogether, the results of cellular targeting studies provide a proof-of-principle for our NtraC model and highlight the particular functional importance of the predicted transition area, which critically affects the rate of protein export. In conclusion, the NtraC approach enables the systematic detection and prediction of cryptic targeting signals present in one coherent sequence, and provides a structurally motivated basis for decoding the functional complexity of long protein targeting signals. Jan Alexander Hiß; Eduard Resch; Alexander Schreiner; Michael Meissner; Anna Starzinski-Powitz; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6012 Wed, 12 Nov 2008 12:55:12 +0100 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6011 Exported proteases of Helicobacter pylori (H. pylori) are potentially involved in pathogen-associated disorders leading to gastric inflammation and neoplasia. By comprehensive sequence screening of the H. pylori proteome for predicted secreted proteases, we retrieved several candidate genes. We detected caseinolytic activities of several such proteases, which are released independently from the H. pylori type IV secretion system encoded by the cag pathogenicity island (cagPAI). Among these, we found the predicted serine protease HtrA (Hp1019), which was previously identified in the bacterial secretome of H. pylori. Importantly, we further found that the H. pylori genes hp1018 and hp1019 represent a single gene likely coding for an exported protein. Here, we directly verified proteolytic activity of HtrA in vitro and identified the HtrA protease in zymograms by mass spectrometry. Overexpressed and purified HtrA exhibited pronounced proteolytic activity, which is inactivated after mutation of Ser205 to alanine in the predicted active center of HtrA. These data demonstrate that H. pylori secretes HtrA as an active protease, which might represent a novel candidate target for therapeutic intervention strategies. Martin Löwer; Christiane Weydig; Dirk Metzler; Andreas Reuter; Anna Starzinski-Powitz; Silja Wessler; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/6011 Wed, 12 Nov 2008 12:34:10 +0100 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5839 We performed a bioinformatical analysis of protein export elements (PEXEL) in the putative proteome of the malaria parasite Plasmodium falciparum. A protein family-specific conservation of physicochemical residue profiles was found for PEXEL-flanking sequence regions. We demonstrate that the family members can be clustered based on the flanking regions only and display characteristic hydrophobicity patterns. This raises the possibility that the flanking regions may contain additional information for a family-specific role of PEXEL. We further show that signal peptide cleavage results in a positional alignment of PEXEL from both proteins with, and without, a signal peptide. Jan Alexander Hiß; Jude Marek Przyborski; Florian Schwarte; Klaus Lingelbach; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5839 Thu, 25 Sep 2008 14:38:10 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/96 Poster presentation In pharmaceutical research and drug development, machine learning methods play an important role in virtual screening and ADME/Tox prediction. For the application of such methods, a formal measure of similarity between molecules is essential. Such a measure, in turn, depends on the underlying molecular representation. Input samples have traditionally been modeled as vectors. Consequently, molecules are represented to machine learning algorithms in a vectorized form using molecular descriptors. While this approach is straightforward, it has its shortcomings. Amongst others, the interpretation of the learned model can be difficult, e.g. when using fingerprints or hashing. Structured representations of the input constitute an alternative to vector based representations, a trend in machine learning over the last years. For molecules, there is a rich choice of such representations. Popular examples include the molecular graph, molecular shape and the electrostatic field. We have developed a molecular similarity measure defined directly on the (annotated) molecular graph, a long-standing established topological model for molecules. It is based on the concepts of optimal atom assignments and iterative graph similarity. In the latter, two atoms are considered similar if their neighbors are similar. This recursive definition leads to a non-linear system of equations. We show how to iteratively solve these equations and give bounds on the computational complexity of the procedure. Advantages of our similarity measure include interpretability (atoms of two molecules are assigned to each other, each pair with a score expressing local similarity; this can be visualized to show similar regions of two molecules and the degree of their similarity) and the possibility to introduce knowledge about the target where available. We retrospectively tested our similarity measure using support vector machines for virtual screening on several pharmaceutical and toxicological datasets, with encouraging results. Prospective studies are under way. Matthias Rupp; Ewgenij Proschak; Gisbert Schneider article http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/96 Tue, 08 Apr 2008 14:21:13 +0200