TY  - JOUR
A1  - Kunz, Meik
A1  - Liang, Chunguang
A1  - Nilla, Santosh
A1  - Cecil, Alexander
A1  - Dandekar, Thomas
T1  - The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development
JF  - Database
N2  - The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure–activity relationships.
KW  - drug-minded protein
KW  - database
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147369
VL  - 2016
ER  - 
TY  - JOUR
A1  - Cecil, Alexander
A1  - Rikanovic, Carina
A1  - Ohlsen, Knut
A1  - Liang, Chunguang
A1  - Bernhardt, Jorg
A1  - Oelschlaeger, Tobias A.
A1  - Gulder, Tanja
A1  - Bringmann, Gerd
A1  - Holzgrabe, Ulrike
A1  - Unger, Matthias
A1  - Dandekar, Thomas
T1  - Modeling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells
N2  - Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine. Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity. Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics.
KW  - Staphylococcus aureus
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-68802
ER  - 
TY  - JOUR
A1  - Cecil, Alexander
A1  - Gentschev, Ivaylo
A1  - Adelfinger, Marion
A1  - Dandekar, Thomas
A1  - Szalay, Aladar A.
T1  - Vaccinia virus injected human tumors: oncolytic virus efficiency predicted by antigen profiling analysis fitted boolean models
JF  - Bioengineered
N2  - Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculate differences in the tumor growth signaling network by tumor type and gender. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, the T-killer cell mediated cell death, Interferon and Interleukin signaling networks. The in silico findings conform very well with in vivo findings of tumor growth. Similar to a previously published analysis of vaccinia virus-injected canine tumors, we were able to confirm the suitability of our boolean modeling for prediction of human tumor growth after virus infection in the current study as well. In summary, these findings indicate that our boolean models could be a useful tool for testing of the efficacy of VACV-mediated cancer therapy already before its use in human patients.
KW  - boolean modeling
KW  - oncolytic virus
KW  - human xenografted mouse models
KW  - cancer therapy
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200507
VL  - 10
IS  - 1
ER  -