Mohamed Diwan M. AbdulHameed

Exploring polypharmacology using a ROCS-based target fishing approach.

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Combined 3D-QSAR Modeling and Molecular Docking Study on Indolinone Derivatives as Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase-1

3-Phosphoinositide-dependent protein kinase-1 (PDK1) is a promising target for developing novel anticancer drugs. In order to understand the structure-activity correlation of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2)=0.907; q(2)=0.737) and CoMSIA model (r(2)=0.991; q(2)=0.824), for predicting the biological activity of new compounds. The detailed microscopic structures of PDK1 binding with inhibitors have been studied by molecular docking. We have also developed docking-based 3D-QSAR models (CoMFA with q(2)=0.729; CoMSIA with q(2)=0.79). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. This is the first report on 3D-QSAR modeling of PDK1 inhibitors. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained PDK1-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.

Understanding Microscopic Binding of Human Microsomal Prostaglandin E Synthase-1 (mPGES-1) Trimer with Substrate PGH2 and Cofactor GSH: Insights from Computational Alanine Scanning and Site-directed Mutagenesis

Microsomal prostaglandin E synthase-1 (mPGES-1) is an essential enzyme involved in a variety of diseases and is the most promising target for the design of next-generation anti-inflammatory drugs. In order to establish a solid structural base, we recently developed a model of mPGES-1 trimer structure by using available crystal structures of both microsomal glutathione transferase-1 (MGST1) and ba3-cytochrome c oxidase as templates. The mPGES-1 trimer model has been used in the present study to examine the detailed binding of mPGES-1 trimer with substrate PGH(2) and cofactor GSH. Results obtained from the computational alanine scanning reveal the contribution of each residue at the protein-ligand interaction interface to the binding affinity, and the computational predictions are supported by the data obtained from the corresponding wet experimental tests. We have also compared our mPGES-1 trimer model with other available 3D models, including an alternative homology model and a low-resolution crystal structure, and found that our mPGES-1 trimer model based on the crystal structures of both MGST1 and ba3-cytochrome c oxidase is more reasonable than the other homology model of mPGES-1 trimer constructed by simply using a low-resolution crystal structure of MGST1 trimer alone as a template. The available low-resolution crystal structure of mPGES-1 trimer represents a closed conformation of the enzyme and thus is not suitable for studying mPGES-1 binding with ligands. Our mPGES-1 trimer model represents a reasonable open conformation of the enzyme and is therefore promising for studying mPGES-1 binding with ligands in future structure-based drug design targeting mPGES-1.

QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ~200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ~76%, representing a 1.5-2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available.

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Synthesis and Evaluation of Strychnos Alkaloids as MDR Reversal Agents for Cancer Cell Eradication

Natural products represent the fourth generation of multidrug resistance (MDR) reversal agents that resensitize MDR cancer cells overexpressing P-glycoprotein (Pgp) to cytotoxic agents. We have developed an effective synthetic route to prepare various Strychnos alkaloids and their derivatives. Molecular modeling of these alkaloids docked to a homology model of Pgp was employed to optimize ligand-protein interactions and design analogues with increased affinity to Pgp. Moreover, the compounds were evaluated for their (1) binding affinity to Pgp by fluorescence quenching, and (2) MDR reversal activity using a panel of in vitro and cell-based assays and compared to verapamil, a known inhibitor of Pgp activity. Compound 7 revealed the highest affinity to Pgp of all Strychnos congeners (Kd=4.4μM), the strongest inhibition of Pgp ATPase activity, and the strongest MDR reversal effect in two Pgp-expressing cell lines. Altogether, our findings suggest the clinical potential of these synthesized compounds as viable Pgp modulators justifies further investigation.

Human Microsomal Prostaglandin E Synthase-1 (mPGES-1) Binding with Inhibitors and the Quantitative Structure-Activity Correlation

The detailed structures of microsomal prostaglandin E synthase-1 (mPGES-1) binding with inhibitors have been studied, for the first time, by using a newly developed computational three-dimensional (3D) structural model of mPGES-1 along with a 3D-quantitative structure--activity relationship (3D-QSAR) analysis. The obtained satisfactory binding structures and 3D-QSAR models strongly suggest that the 3D structural model of mPGES-1 is reasonable for study of mPGES-1 binding with inhibitors and for future design of novel mPGES-1 inhibitors.

Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

Understanding Microscopic Binding of Human Microsomal Prostaglandin E Synthase-1 with Substrates and Inhibitors by Molecular Modeling and Dynamics Simulation

Microsomal prostaglandin E synthase-1 (mPGES-1) is a promising target for development of next-generation anti-inflammatory drugs. It is crucial for rational design of the next-generation anti-inflammatory drugs to know the three-dimensional (3D) structure of mPGES-1 trimer and to understand how mPGES-1 binds with substrates and inhibitors. In the current work, a 3D structural model of human mPGES-1 trimer has been developed, for the first time, by performing combined homology modeling, molecular docking, and molecular dynamics simulation. The 3D structural model enables us to understand how mPGES-1 binds with its substrates/inhibitors, and the key amino acid residues for the mPGES-1 binding with ligands have been identified. The detailed 3D structures and calculated binding free energies for mPGES-1s binding with substrates and inhibitors are all consistent with available experimental data, suggesting that the 3D model of the mPGES-1 trimer and the enzyme-ligand binding modes are reasonable. The new structural insights obtained from this study should be valuable for rational design of next-generation anti-inflammatory drugs.

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

BackgroundThe current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinskis rule-of-five for drug-likeness and Opreas criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.ResultsThe method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Healths Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.ConclusionsOur proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.