Gregory Tawa

Computation of 3D queries for ROCS based virtual screens

Rapid overlay of chemical structures (ROCS) is a method that aligns molecules based on shape and/or chemical similarity. It is often used in 3D ligand-based virtual screening. Given a query consisting of a single conformation of an active molecule ROCS can generate highly enriched hit lists. Typically the chosen query conformation is a minimum energy structure. Can better enrichment be obtained using conformations other than the minimum energy structure? To answer this question a methodology has been developed called CORAL (COnformational analysis, Rocs ALignment). For a given set of molecule conformations it computes optimized conformations for ROCS screening. It does so by clustering all conformations of a chosen molecule set using pairwise ROCS combo scores. The best representative conformation is that which has the highest average overlap with the rest of the conformations in the cluster. It is these best representative conformations that are then used for virtual screening. CORAL was tested by performing virtual screening experiments with the 40 DUD (Directory of Useful Decoys) data sets. Both CORAL and minimum energy queries were used. The recognition capability of each query was quantified as the area under the ROC curve (AUC). Results show that the CORAL AUC values are on average larger than the minimum energy AUC values. This demonstrates that one can indeed obtain better ROCS enrichments with conformations other than the minimum energy structure. As a result, CORAL analysis can be a valuable first step in virtual screening workflows using ROCS.

Modeling G protein-coupled receptors for structure-based drug discovery using low-frequency normal modes for refinement of homology models: Application to H3 antagonists

G Protein‐Coupled Receptors (GPCRs) are integral membrane proteins that play important role in regulating key physiological functions, and are targets of about 50% of all recently launched drugs. High‐resolution experimental structures are available only for very few GPCRs. As a result, structure‐based drug design efforts for GPCRs continue to rely on in silico modeling, which is considered to be an extremely difficult task especially for these receptors. Here, we describe Gmodel, a novel approach for building 3D atomic models of GPCRs using a normal mode‐based refinement of homology models. Gmodel uses a small set of relevant low‐frequency vibrational modes derived from Random Elastic Network model to efficiently sample the large‐scale receptor conformation changes and generate an ensemble of alternative models. These are used to assemble receptor–ligand complexes by docking a known active into each of the alternative models. Each of these is next filtered using restraints derived from known mutation and binding affinity data and is refined in the presence of the active ligand. In this study, Gmodel was applied to generate models of the antagonist form of histamine 3 (H3) receptor. The validity of this novel modeling approach is demonstrated by performing virtual screening (using the refined models) that consistently produces highly enriched hit lists. The models are further validated by analyzing the available SAR related to classical H3 antagonists, and are found to be in good agreement with the available experimental data, thus providing novel insights into the receptor–ligand interactions. Proteins 2010.

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.

3,4-Dihydropyrimido(1,2-a)indol-10(2H)-ones as potent non-peptidic inhibitors of caspase-3

Cysteine-dependant aspartyl protease (caspase) activation has been implicated as a part of the signal transduction pathway leading to apoptosis. It has been postulated that caspase-3 inhibition could attenuate cell damage after an ischemic event and thereby providing for a novel neuroprotective treatment for stroke. As part of a program to develop a small molecule inhibitor of caspase-3, a novel series of 3,4-dihydropyrimido(1,2-a)indol-10(2H)-ones (pyrimidoindolones) was identified. The synthesis, biological evaluation and structure-activity relationships of the pyrimidoindolones are described.

Exploiting large-scale drug-protein interaction information for computational drug repurposing

BackgroundDespite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated.ResultsWe developed a computational method termed “drug-protein interaction-based repurposing” (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect.ConclusionsDetailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta‐analysis of four distinct high‐throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein‐interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic‐like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high‐yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI.

Benzimidazole- and indole-substituted 1,3′-bipyrrolidine benzamides as histamine H3 receptor antagonists

Using a focused screen of biogenic amine compounds we identified a novel series of H(3)R antagonists. A preliminary SAR study led to reduction of MW while increasing binding affinity and potency. Optimization of the physical properties of the series led to (S)-6n, with improved brain to plasma exposure and efficacy in both water intake and novel object recognition models.

Identification of a new class of small molecule C5a receptor antagonists.

C5a is a terminal product of the complement cascade that activates and attracts inflammatory cells including granulocytes, mast cells and macrophages via a specific GPCR, the C5a receptor (C5aR). Inhibition of C5a/C5aR interaction has been shown to be efficacious in several animal models of autoimmune diseases, including RA, SLE and asthma. This account reports the discovery of a new class of C5aR antagonists through high-throughput screening. The lead compounds in this series are selective and block C5a binding, C5a-promoted calcium flux in human neutrophils with nanomolar potency.

Ion Channel Screening Plates: Design, Construction, and Maintenance

Ion channels have provided a diverse set of therapeutic targets across all areas of the pharmaceutical industry. Many companies are pursuing this unique class of targets for areas of unmet medical need such as neuropathic and inflammatory pains. In the past, focused library screening sets had been designed for CNS and kinase targets. Our investigations were aimed at creating a similar dynamic screening set enriched for compounds targeting ion channels to aid screening efforts of this important class of targets. The key advantages of this approach for ion channel targets would be: (1) to identify tool compounds for novel targets and assist in assay validation, (2) to serve as a focused screen for non-384-well adaptable targets, and (3) to jump start a particular program, that is, catch-up to competition for validated, well-known targets.