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- Fuzzy virtual ligands for virtual screening (2009)
- 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 : LIQUID  fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker . 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.
- Prediction of extracellular proteases of the human pathogen Helicobacter pylori reveals proteolytic activity of the Hp1018/19 protein HtrA (2008)
- 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.
- Correction: Prediction of type III secretion signals in genomes of gram-negative bacteria (2009)
- 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.
- Inhibitors of Helicobacter pylori protease HtrA found by "virtual ligand" screening combat bacterial invasion of epithelia (2011)
- 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.
- Prediction of type III secretion signals in genomes of gram-negative bacteria (2009)
- 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 ).