Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs

Toxicity Testing of Effluent-Dominated Stream Using Predictive Molecular-Level Toxicity Signatures Based on High-Resolution Mass Spectrometry: A Case Study of the Lubbock Canyon Lake System

Current aquatic toxicity assessments usually focus on targeted analyses coupled with toxicity testing to determine the impacts of complex mixtures on aquatic organisms. However, based on this approach alone, it is sometimes difficult to explain observed toxicity from the selected chemical analytes. Recent analytical advances such as high-resolution mass spectrometry (HRMS) can improve the characterizations of the chemical composition of complex mixtures, but the intensive labor required to produce confident identifications limits its utility in high-throughput screening. In the present study, we evaluated a rapid workflow to predict potential toxicity signatures of complex water samples based on high-throughput, tentative HRMS identifications derived from database matching, followed by identification of chemical-ligand interactions and pathway identification. We tested the workflow with water samples from the effluent-dominated Lubbock Canyon Lake System (LCLS). Results across all sites showed that predicted toxicity signatures had little variation when correcting for HRMS false-positive rates. The most common pathways across sites were gonadotropin-releasing hormone receptor and α-adrenergic receptor signaling. Alterations to the predicted pathways were successfully observed in larval zebrafish exposures to LCLS water samples. These results may allow researchers to better utilize rapid assessments of HRMS data for the assessment of adverse impacts on aquatic organisms.

New small-molecule compound Hu-17 inhibits estrogen biosynthesis by aromatase in human ovarian granulosa cancer cells

Estrogen-dependent cancers (breast, endometrial, and ovarian) are among the leading causes of morbidity and mortality in women worldwide. Aromatase is the main enzyme that catalyzes the biosynthesis of estrogen, which drives proliferation, and antiestrogens can inhibit the growth of these estrogen-dependent cancers. Hu-17, an aromatase inhibitor, is a novel small-molecule compound that suppresses viability of and promotes apoptosis in ovarian cancer cells. Therefore, this study aimed to predict targets of Hu-17 and assess its intracellular signaling in ovarian cancer cells. Using the Similarity Ensemble Approach software to predict the potential mechanism of Hu-17 and combining phospho-proteome arrays with western blot analysis, we observed that Hu-17 could inhibit the ERK pathway, resulting in reduced estrogen synthesis in KGN cells, a cell line derived from a patient with invasive ovarian granulosa cell carcinoma. Hu-17 reduced the expression of CYP19A1 mRNA, responsible for producing aromatase, by suppressing the phosphorylation of cAMP response element binding-1. Hu-17 also accelerated aromatase protein degradation but had no effect on aromatase activity. Therefore, Hu-17 could serve as a potential treatment for estrogen-dependent cancers albeit further investigation is warranted.

Predicted Biological Activity of Purchasable Chemical Space

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL’s bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org.

Rare Diseases: Drug Discovery and Informatics Resource

A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.

A Simple Representation of Three-Dimensional Molecular Structure

Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the extended connectivity fingerprint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the extended three-dimensional fingerprint (E3FP). By integrating E3FP with the similarity ensemble approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442-0.637 kcal/mol/heavy atom.

Design and synthesis of some new 2,3′-bipyridine-5-carbonitriles as potential anti-inflammatory/antimicrobial agents

Aim: Inflammation may cause accumulation of fluid in the injured area, which may promote bacterial growth. Other reports disclosed that non-steroidal anti-inflammatory drugs may enhance progression of bacterial infection. Results: This work describes synthesis of new series of 2,3′-bipyridine-5-carbonitriles as structural analogs of etoricoxib, linked at position-6 to variously substituted thio or oxo moieties. Biological screening results revealed that compounds 2b, 4b, 7e and 8 showed significant acute and chronic AI activities and broad spectrum of antimicrobial activity. In addition, similarity ensemble approach was applied to predict potential biological targets of the tested compounds. Then, pharmacophore modeling study was employed to determine the most important structural parameters controlling bioactivity. Moreover, title compounds showed physicochemical properties within those considered adequate for drug candidates. Conclusion: This study explored the potential of such series of compounds as structural leads for further modification to develop a new class of dual AI-antimicrobial agents.

Systems Pharmacology in Small Molecular Drug Discovery

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference

Most existing antibiotics were discovered through screens of environmental microbes, particularly the streptomycetes, for the capacity to prevent the growth of pathogenic bacteria. This "activity-guided screening" method has been largely abandoned because it repeatedly rediscovers those compounds that are highly expressed during laboratory culture. Most of these metabolites have already been biochemically characterized. However, the sequencing of streptomycete genomes has revealed a large number of "cryptic" secondary metabolic genes that are either poorly expressed in the laboratory or that have biological activities that cannot be discovered through standard activity-guided screens. Methods that reveal these uncharacterized compounds, particularly methods that are not biased in favor of the highly expressed metabolites, would provide direct access to a large number of potentially useful biologically active small molecules. To address this need, we have devised a discovery method in which a chemical elicitor called Cl-ARC is used to elevate the expression of cryptic biosynthetic genes. We show that the resulting change in product yield permits the direct discovery of secondary metabolites without requiring knowledge of their biological activity. We used this approach to identify three rare secondary metabolites and find that two of them target eukaryotic cells and not bacterial cells. In parallel, we report the first paired use of cheminformatic inference and chemical genetic epistasis in yeast to identify the target. In this way, we demonstrate that oxohygrolidin, one of the eukaryote-active compounds we identified through activity-independent screening, targets the V1 ATPase in yeast and human cells and secondarily HSP90.

Target prediction utilising negative bioactivity data covering large chemical space

BACKGROUND

In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.

RESULTS

A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76.

CONCLUSIONS

The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.

A combination of metabolic labeling and 2D-DIGE analysis in response to a farnesyltransferase inhibitor facilitates the discovery of new prenylated proteins

Protein prenylation is a post-translational modification required for proper cellular localization and activity of many important eukaryotic proteins. Farnesyltransferase inhibitors (FTIs) have been explored extensively for their antitumor activity. To assist in identifying potentially new and more useful markers for therapeutic applications, we developed a strategy that uses a combination of metabolic labeling and 2D DIGE (differential gel electrophoresis) to discover new prenylated proteins whose cellular levels are influenced by FTIs. In this approach, metabolic labeling of prenylated proteins was first carried out with an alkyne-modified isoprenoid analog, C15Alk, in the presence or absence of the FTI L-744,832. The resulting alkyne-tagged proteins were then labeled with Cy3-N3 and Cy5-N3 and subjected to 2D-DIGE. Multiple spots having altered levels of labeling in presence of the FTI were observed. Mass spectrometric analysis of some of the differentially labeled spots identified several known prenylated proteins, along with HisRS, PACN-3, GNAI-1 and GNAI-2, which are not known to be prenylated. In vitro farnesylation of a C-terminal peptide sequence derived from GNAI-1 and GNAI-2 produced a farnesylated product, suggesting GNAI-1 and GNAI-2 are potential novel farnesylated proteins. These results suggest that this new strategy could be useful for the identification of prenylated proteins whose level of post-translational modification has been modulated by the presence of an FTI. Additionally, this approach, which decreases sample complexity and thereby facilitates analysis, should be applicable to studies of other post-translational modifications as well.

Polypharmacology: challenges and opportunities in drug discovery

At present, the legendary magic bullet, i.e., a drug with high potency and selectivity toward a specific biological target, shares the spotlight with an emerging and alternative polypharmacology approach. Polypharmacology suggests that more effective drugs can be developed by specifically modulating multiple targets. It is generally thought that complex diseases such as cancer and central nervous system diseases may require complex therapeutic approaches. In this respect, a drug that "hits" multiple sensitive nodes belonging to a network of interacting targets offers the potential for higher efficacy and may limit drawbacks generally arising from the use of a single-target drug or a combination of multiple drugs. In this review, we will compare advantages and disadvantages of multitarget versus combination therapies, discuss potential drug promiscuity arising from off-target effects, comment on drug repurposing, and introduce approaches to the computational design of multitarget drugs.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window

In this study, two probabilistic machine-learning algorithms were compared for in silico target prediction of bioactive molecules, namely the well-established Laplacian-modified Naïve Bayes classifier (NB) and the more recently introduced (to Cheminformatics) Parzen-Rosenblatt Window. Both classifiers were trained in conjunction with circular fingerprints on a large data set of bioactive compounds extracted from ChEMBL, covering 894 human protein targets with more than 155,000 ligand-protein pairs. This data set is also provided as a benchmark data set for future target prediction methods due to its size as well as the number of bioactivity classes it contains. In addition to evaluating the methods, different performance measures were explored. This is not as straightforward as in binary classification settings, due to the number of classes, the possibility of multiple class memberships, and the need to translate model scores into "yes/no" predictions for assessing model performance. Both algorithms achieved a recall of correct targets that exceeds 80% in the top 1% of predictions. Performance depends significantly on the underlying diversity and size of a given class of bioactive compounds, with small classes and low structural similarity affecting both algorithms to different degrees. When tested on an external test set extracted from WOMBAT covering more than 500 targets by excluding all compounds with Tanimoto similarity above 0.8 to compounds from the ChEMBL data set, the current methodologies achieved a recall of 63.3% and 66.6% among the top 1% for Naïve Bayes and Parzen-Rosenblatt Window, respectively. While those numbers seem to indicate lower performance, they are also more realistic for settings where protein targets need to be established for novel chemical substances.

Andrographolide derivatives inhibit guanine nucleotide exchange and abrogate oncogenic Ras function

Aberrant signaling by oncogenic mutant rat sarcoma (Ras) proteins occurs in ∼15% of all human tumors, yet direct inhibition of Ras by small molecules has remained elusive. Recently, several small-molecule ligands have been discovered that directly bind Ras and inhibit its function by interfering with exchange factor binding. However, it is unclear whether, or how, these ligands could lead to drugs that act against constitutively active oncogenic mutant Ras. Using a dynamics-based pocket identification scheme, ensemble docking, and innovative cell-based assays, here we show that andrographolide (AGP)--a bicyclic diterpenoid lactone isolated from Andrographis paniculata--and its benzylidene derivatives bind to transient pockets on Kirsten-Ras (K-Ras) and inhibit GDP-GTP exchange. As expected for inhibitors of exchange factor binding, AGP derivatives reduced GTP loading of wild-type K-Ras in response to acute EGF stimulation with a concomitant reduction in MAPK activation. Remarkably, however, prolonged treatment with AGP derivatives also reduced GTP loading of, and signal transmission by, oncogenic mutant K-RasG12V. In sum, the combined analysis of our computational and cell biology results show that AGP derivatives directly bind Ras, block GDP-GTP exchange, and inhibit both wild-type and oncogenic K-Ras signaling. Importantly, our findings not only show that nucleotide exchange factors are required for oncogenic Ras signaling but also demonstrate that inhibiting nucleotide exchange is a valid approach to abrogating the function of oncogenic mutant Ras.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Chemical informatics uncovers a new role for moexipril as a novel inhibitor of cAMP phosphodiesterase-4 (PDE4)

PDE4 is one of eleven known cyclic nucleotide phosphodiesterase families and plays a pivotal role in mediating hydrolytic degradation of the important cyclic nucleotide second messenger, cyclic 3′5′ adenosine monophosphate (cAMP). PDE4 inhibitors are known to have anti-inflammatory properties, but their use in the clinic has been hampered by mechanism-associated side effects that limit maximally tolerated doses. In an attempt to initiate the development of better-tolerated PDE4 inhibitors we have surveyed existing approved drugs for PDE4-inhibitory activity. With this objective, we utilised a high-throughput computational approach that identified moexipril, a well tolerated and safe angiotensin-converting enzyme (ACE) inhibitor, as a PDE4 inhibitor. Experimentally we showed that moexipril and two structurally related analogues acted in the micro molar range to inhibit PDE4 activity. Employing a FRET-based biosensor constructed from the nucleotide binding domain of the type 1 exchange protein activated by cAMP, EPAC1, we demonstrated that moexipril markedly potentiated the ability of forskolin to increase intracellular cAMP levels. Finally, we demonstrated that the PDE4 inhibitory effect of moexipril is functionally able to induce phosphorylation of the small heat shock protein, Hsp20, by cAMP dependent protein kinase A. Our data suggest that moexipril is a bona fide PDE4 inhibitor that may provide the starting point for development of novel PDE4 inhibitors with an improved therapeutic window.

On the origins of drug polypharmacology

The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?

Small molecule inhibitors of Smoothened ciliary localization and ciliogenesis

Vertebrate Hedgehog (Hh) signals involved in development and some forms of cancer, such as basal cell carcinoma, are transduced by the primary cilium, a microtubular projection found on many cells. A critical step in vertebrate Hh signal transduction is the regulated movement of Smoothened (Smo), a seven-transmembrane protein, to the primary cilium. To identify small molecules that interfere with either the ciliary localization of Smo or ciliogenesis, we undertook a high-throughput, microscopy-based screen for compounds that alter the ciliary localization of YFP-tagged Smo. This screen identified 10 compounds that inhibit Hh pathway activity. Nine of these Smo antagonists (SA1-9) bind Smo, and one (SA10) does not. We also identified two compounds that inhibit ciliary biogenesis, which block microtubule polymerization or alter centrosome composition. Differential labeling of cell surface and intracellular Smo pools indicates that SA1-7 and 10 specifically inhibit trafficking of intracellular Smo to cilia. In contrast, SA8 and 9 recruit endogenous Smo to the cilium in some cell types. Despite these different mechanisms of action, all of the SAs inhibit activation of the Hh pathway by an oncogenic form of Smo, and abrogate the proliferation of basal cell carcinoma-like cancer cells. The SA compounds may provide alternative means of inhibiting pathogenic Hh signaling, and our study reveals that different pools of Smo move into cilia through distinct mechanisms.

An enzyme-coupled continuous fluorescence assay for farnesyl diphosphate synthases

Farnesyl diphosphate synthase (FDPS) catalyzes the conversion of isopentenyl diphosphate and dimethylallyl diphosphate to farnesyl diphosphate, a crucial metabolic intermediate in the synthesis of cholesterol, ubiquinone, and prenylated proteins; consequently, much effort has gone into developing inhibitors that target FDPS. Currently most FDPS assays either use radiolabeled substrates and are discontinuous or monitor pyrophosphate release and not farnesyl diphosphate (FPP) creation. Here we report the development of a continuous coupled enzyme assay for FDPS activity that involves the subsequent incorporation of the FPP product of that reaction into a peptide via the action of protein farnesyltransferase (PFTase). By using a dansylated peptide whose fluorescence quantum yield increases upon farnesylation, the rate of FDPS-catalyzed FPP production can be measured. We show that this assay is more sensitive than existing coupled assays, that it can be used to conveniently monitor FDPS activity in a 96-well plate format, and that it can reproduce IC(50) values for several previously reported FDPS inhibitors. This new method offers a simple, safe, and continuous method to assay FDPS activity that should greatly facilitate the screening of inhibitors of this important target.

Non-bisphosphonate inhibitors of isoprenoid biosynthesis identified via computer-aided drug design

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver operating characteristic plots (ROC-AUC) revealed that scoring with SVM-SP resulted in consistently better enrichment over all target families, outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (1-3) inhibited kinase activity in vitro with IC₅₀ of 58, 2, and 10 μM. In cell cultures, compounds 1-3 inhibited nonsmall cell lung carcinoma (H1299) cancer cell proliferation with similar IC₅₀ values for compound 3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC₅₀ of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.

Strategies to discover unexpected targets for drugs active at G protein-coupled receptors

G protein-coupled receptors (GPCRs) are an evolutionarily conserved family of signaling molecules comprising approximately 2% of the human genome; this receptor family remains a central focus in basic pharmacology studies and drug discovery efforts. Detailed studies of drug action at GPCRs over the past decade have revealed existing and novel ligands that exhibit polypharmacology-that is, drugs with activity at more than one receptor target for which they were designed. These "off-target" drug actions can be a liability that causes adverse side effects; however, in several cases, drugs with less selectivity demonstrate better clinical efficacy. Here we review physical screening and cheminformatic approaches that define drug activity at the GPCR receptorome. In many cases, such profiling has revealed unexpected targets that explain therapeutic actions as well as off-targets underlying drug side effects. Such drug-receptor profiling has also provided new insights into mechanisms of action of existing drugs and has suggested directions for future drug development.

The role of translational bioinformatics in drug discovery

The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical data and patient records has informed decision-making in multiple aspects of drug discovery and development. Here, we review key examples of translational bioinformatics approaches to emphasize its potential to enhance the quality of drug discovery pipelines, reduce attrition rates and, ultimately, lead to more effective treatments.

The chemical basis of pharmacology

Molecular biology now dominates pharmacology so thoroughly that it is difficult to recall that only a generation ago the field was very different. To understand drug action today, we characterize the targets through which they act and new drug leads are discovered on the basis of target structure and function. Until the mid-1980s the information often flowed in reverse: investigators began with organic molecules and sought targets, relating receptors not by sequence or structure but by their ligands. Recently, investigators have returned to this chemical view of biology, bringing to it systematic and quantitative methods of relating targets by their ligands. This has allowed the discovery of new targets for established drugs, suggested the bases for their side effects, and predicted the molecular targets underlying phenotypic screens. The bases for these new methods, some of their successes and liabilities, and new opportunities for their use are described.