Automated Protocol for Large-Scale Modeling of Gene Expression Data

Ligand-Based Drug Design of Novel Antimicrobials against Staphylococcus aureus by Targeting Bacterial Transcription

Staphylococcus aureus is a common human commensal pathogen that causes a wide range of infectious diseases. Due to the generation of antimicrobial resistance, the pathogen becomes resistant to more and more antibiotics, resulting in methicillin-resistant S. aureus (MRSA) and even multidrug-resistant S. aureus (MDRSA), namely 'superbugs'. This situation highlights the urgent need for novel antimicrobials. Bacterial transcription, which is responsible for bacterial RNA synthesis, is a valid but underutilized target for developing antimicrobials. Previously, we reported a novel class of antimicrobials, coined nusbiarylins, that inhibited bacterial transcription by interrupting the protein-protein interaction (PPI) between two transcription factors NusB and NusE. In this work, we developed a ligand-based workflow based on the chemical structures of nusbiarylins and their activity against S. aureus. The ligand-based models-including the pharmacophore model, 3D QSAR, AutoQSAR, and ADME/T calculation-were integrated and used in the following virtual screening of the ChemDiv PPI database. As a result, four compounds, including J098-0498, 1067-0401, M013-0558, and F186-026, were identified as potential antimicrobials against S. aureus, with predicted pMIC values ranging from 3.8 to 4.2. The docking study showed that these molecules bound to NusB tightly with the binding free energy ranging from -58 to -66 kcal/mol.

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.