Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

The challenges of developing novel antiparasitic drugs

Only a few novel classes of antiparasitic drugs have emerged over the last few decades, reflecting the difficulties associated with bringing a safe, effective molecule to market. In recent years, the screening paradigm has shifted from empirical whole parasite screening towards mechanism-based high throughput screening. This approach requires investment in molecular parasitology and in understanding the basic biology of parasites, as well as requiring considerable investment in an infrastructure for screening. Add to this the fact that the drug discovery process is iterative with high attrition, the Animal Health industry by necessity must focus on discovering medicines for diseases, which will deliver a return on investment. In recent years the rapid progression of genomics has unlocked a plethora of tools for target identification, validation and screening, revolutionising mechanism-based screening for antiparasitic drug discovery. The challenge still remains; however, to identify novel chemical entities with the properties required to deliver a safe, effective antiparasitic drug.     

Multiplex GPCR assay in reverse transfection cell microarrays

G protein-coupled receptors (GPCRs) are a superfamily of proteins that include some of the most important drug targets in the pharmaceutical industry. Despite the success of this group of drugs, there remains a need to identify GPCR-targeted drugs with greater selectivity, to develop screening assays for validated targets, and to identify ligands for orphan receptors. To address these challenges, the authors have created a multiplexed GPCR assay that measures greater than 3000 receptor: ligand interactions in a single microplate. The multiplexed assay is generated by combining reverse transfection in a 96-well plate format with a calcium flux readout. This assay quantitatively measures receptor activation and inhibition and permits the determination of compound potency and selectivity for entire families of GPCRs in parallel. To expand the number of GPCR targets that may be screened in this system, receptors are cotransfected with plasmids encoding a promiscuous G protein, permitting the analysis of receptors that do not normally mobilize intracellular calcium upon activation. The authors demonstrate the utility of reverse transfection cell microarrays to GPCR-targeted drug discovery with examples of ligand selectivity screening against a panel of GPCRs as well as dose-dependent titrations of selected agonists and antagonists.     

Functional assays for screening GPCR targets

G-protein-coupled receptors (GPCRs) are valuable molecular targets for drug discovery. An important aspect of the early drug discovery process is the design and implementation of high-throughput GPCR functional assays that allow the cost-effective screening of large compound libraries to identify novel drug candidates. Several functional assay kits based on fluorescence and/or chemiluminescence detection are commercially available for convenient screen development, each having advantages and disadvantages. In addition, new GPCR biosensors and high-content imaging technologies have recently been developed that hold promise for the development of functional GPCR screens in living cells.     

Multiplexed G-protein-coupled receptor Ca2+ flux assays for high-throughput screening

An early drug discovery approach focusing on gene families can benefit from strategies that exploit common signaling mechanisms to more effectively identify and characterize novel chemical lead structures. Multiplexing, defined as the screening of multiple targets within the same experiment, is an example of this strategy. Here, the authors describe a technique that allows multiplexing of a common assay type used to study G-protein-coupled receptors: changes in intracellular Ca2+ levels as measured by Molecular Device's fluorometric imaging plate reader (FLIPR). The multiplexed FLIPR assays showed the expected pharmacological properties of single assays, with good reproducibility and Z* factors. The authors used them to screen large compound libraries in 2 multiplexed assay designs. The 1st used a single-cell line expressing 2 different receptors and the 2nd a mixture of 2 cell lines of the same type each expressing distinct receptors. Screening using these multiplexed assays produced significant savings in reagents, time, and human resources and allowed the authors to quickly identify specific and selective hits.     

Structural genomics of GPCRs

G protein-coupled receptors (GPCRs) are targets for 60-70% of drugs in development today. Traditionally, the drug discovery process has relied on screening of chemical compounds to identify novel and more-efficient drug molecules. Structure-based drug design, however, provides a targeted approach but has been severely hampered by limited knowledge of high-resolution structures of GPCRs owing to the difficulties encountered in their expression, purification and crystallization. In addition to individual laboratories studying specific GPCRs, structural genomics initiatives have been established as large networks with a wide range of expertise in protein expression, purification and crystallography. Several of these national and international consortia have included GPCRs in their programs. Milligram quantities of GPCRs can now be expressed in several expression systems and purified to high homogeneity. However, success in crystallization still requires major technological improvement.     

Affinity selection-mass spectrometry screening techniques for small molecule drug discovery

Affinity selection-mass spectrometry (AS-MS) techniques assess the binding of candidate molecules to immobilized or soluble receptors, and these methods are gaining acceptance in high throughput screening laboratories as valuable complements to traditional drug discovery technologies. A diversity of receptor types have been evaluated by AS-MS, including those that are difficult to screen using traditional biochemical approaches. AS-MS techniques that couple liquid chromatography-MS with size-based separation methods, such as ultrafiltration, gel permeation, or size-exclusion chromatography, are particularly amenable to the demands of MS-based screening and have demonstrated the greatest success across a broad range of drug targets. MS measurements of receptor function have many of the same advantages as AS-MS screening and are increasingly used for drug discovery as well.     

Minireview: Challenges of High Throughput Screening Against Cell Surface Receptors

Abstract

Excessive or inappropriate activation of cell surface receptors can mediate the development of disease. Receptors, therefore, are a focus for drug discovery activities. Empirical screening is important in the search for novel compounds acting at receptors. Technical developments and the application of molecular biology have facilitated access to receptors of interest and have provided efficient screening methods capable of very high throughput. Reliability in high throughput screening requires the use of appropriate methodology, good screen design and effective validation and quality control processes. Validation should aim to establish that the basic experimental design is sound. In developing software to handle high throughput screening data, a fundamental requirement is to provide performance monitoring and error trapping facilities. Additional requirements are automatic data capture from instruments, on-line data reduction and analysis and transfer of results to central databases. As data volumes increase through effective high throughput screening, conventional interrogation methods become less appropriate and are being augmented by newer computing techniques referred to as knowledge mapping or database mining. Targeting cell surface receptors has been very successful as an approach to drug discovery. If the challenges of high throughput empirical screening are addressed effectively, cell surface receptors will provide new opportunities for improved therapy in the coming years.     

Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor

Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.     

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.     

The cell cycle hypothesis of Alzheimer's disease: suggestions for drug development

The cell cycle of hypothesis of neural dysfunction in chronic neurodegenerative conditions such as Alzheimer's disease (AD) offers a unified approach to understanding both existing and novel strategies for drug development. At the present time, a ligand based approach is a pragmatic solution for identifying new chemical leads on which to base future discovery and optimisation. We have pursued a ligand based approach on the basis of public domain data to identify existing compounds capable of abrogating the cell cycle at the G0-G1 interface. Selected on this basis, irrespective of the tissue under study, we identified several classes of compounds as potential chemical leads. Of these compounds, at least ten have already been shown to be neuroprotective in animal models of acute neurodegeneration. Such compounds could form the basis of a screening exercise after development of suitable screening tools. Progressing of chemical leads through such an approach will be more efficient if future leads display relevant "drug-like" properties. Further, drug development in this arena should take account of the special concerns raised by targeting an elderly population. This will involve accounting for frequent polypharmacy in the aging population, and age-related alterations in physiology.     

Application of mass spectrometry technologies for the discovery of low-molecular weight modulators of enzymes and protein-protein interactions

In recent years, mass spectrometry has gained widespread use as an assay and screening technology in drug discovery because it enables sensitive, label-free detection of low-molecular weight modulators of biomolecules as well as sensitive and accurate detection of high-molecular weight modifications of biomolecules. Electrospray and matrix-assisted laser desorption ionization are the most widely used ionization techniques to identify chemical compounds interfering with enzymatic function, receptor-ligand binding or molecules modulating a protein-protein interaction of interest. Mass spectrometry based techniques are no longer restricted to screening in biochemical assay systems but have now become also applicable to imaging of biomolecules and chemical compounds in cell-based assay systems and even in highly complex tissue sections.     

PREDICT modeling and in-silico screening for G-protein coupled receptors

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.     

Exosome nanovesicles displaying G protein-coupled receptors for drug discovery

Exosomes are naturally occurring nanovesicles that can be tailored to display a broad range of drug targets, including G protein-coupled receptors. Such vesicles provide a new source of complex membrane proteins that are maintained in their native conformation. Given the difficulties to isolate receptors for drug target validation and discovery, receptor presentation on exosome emerges as a promising new tool for drug screening. The potential of this technology is illustrated here with recombinant exosomes presenting the somatostatin receptor 2 as an example. The receptor-containing vesicles were identified as exosomes since they also bear Lactadherin, a hallmark of exosome nanovesicles. The amount of somatostatin receptor 2 on exosomes was similar to the amount of the most abundant known exosome membrane proteins. The receptor was functional and similar in size to the form found on cell surface. Finally, recombinant exosomes were used in several assay formats that exemplify their capacity as a new receptor presentation platform for drug discovery. These include the induction and detection of antibody as well as screening of antibody repertoires without the need to purify membrane proteins.     

Aptamers as reagents for high-throughput screening

The identification of new drug candidates from chemical libraries is a major component of discovery research in many pharmaceutical companies. Given the large size of many conventional and combinatorial libraries and the rapid increase in the number of possible therapeutic targets, the speed with which efficient high-throughput screening (HTS) assays can be developed can be a rate-limiting step in the discovery process. We show here that aptamers, nucleic acids that bind other molecules with high affinity, can be used as versatile reagents in competition binding HTS assays to identify and optimize small-molecule ligands to protein targets. To illustrate this application, we have used labeled aptamers to platelet-derived growth factor B-chain and wheat germ agglutinin to screen two sets of potential small-molecule ligands. In both cases, binding affinities of all ligands tested (small molecules and aptamers) were strongly correlated with their inhibitory potencies in functional assays. The major advantages of using aptamers in HTS assays are speed of aptamer identification, high affinity of aptamers for protein targets, relatively large aptamer-protein interaction surfaces, and compatibility with various labeling/detection strategies. Aptamers may be particularly useful in HTS assays with protein targets that have no known binding partners such as orphan receptors. Since aptamers that bind to proteins are often specific and potent antagonists of protein function, the use of aptamers for target validation can be coupled with their subsequent use in HTS.     

SWEETLEAD: an In Silico Database of Approved Drugs,Regulated Chemicals,and Herbal Isolates for Computer-Aided Drug Discovery

In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead) provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.     

Signal transduction profiling using label-free biosensors

Knowledge of the way in which ligands modulate cellular responses via membrane-associated receptors is of central importance to drug discovery and elucidation of signal transduction pathways. Biophysical label-free methods can be used to characterize ligand and drug candidate interactions with neurotransmitters, cytokine receptors, tyrosine kinase receptors, ligand- and voltage-gated ion channels, G protein-coupled receptors (GPCRs), and antibody receptors. Ligand or drug candidate screening typically involves selecting ligands or subsets of a compound library for analysis, transfecting a cell line overexpressing the target receptor, then monitoring one or two downstream reporters of receptor activation such as Ca²⁺, cAMP, inositol phosphate, etc. Inevitably, this process leads to a data set predicated by these selections. In contrast, label-free screening techniques allow a holistic, pathway-independent screening strategy to provide a functional or phenotypic readout of receptor activation. Detection techniques that measure changes in cell conductance, viscoelastic properties, refractive index, and other optical parameters that are modulated as a consequence of receptor activation are reviewed.     

High-throughput Screening in Embryonic Stem Cell-derived Neurons Identifies Potentiators of α-Amino-3-hydroxyl-5-methyl-4-isoxazolepropionate-type Glutamate Receptors

Stem cell biology offers advantages to investigators seeking to identify new therapeutic molecules. Specifically, stem cells are genetically stable, scalable for molecular screening, and function in cellular assays for drug efficacy and safety. A key hurdle for drug discoverers of central nervous system disease is a lack of high quality neuronal cells. In the central nervous system, α-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate (AMPA) subtype glutamate receptors mediate the vast majority of excitatory neurotransmissions. Embryonic stem (ES) cell protocols were developed to differentiate into neuronal subtypes that express AMPA receptors and were pharmacologically responsive to standard compounds for AMPA potentiation. Therefore, we hypothesized that stem cell-derived neurons should be predictive in high-throughput screens (HTSs). Here, we describe a murine ES cell-based HTS of a 2.4 × 10⁶ compound library, the identification of novel chemical “hits” for AMPA potentiation, structure function relationship of compounds and receptors, and validation of chemical leads in secondary assays using human ES cell-derived neurons. This reporting of murine ES cell derivatives being formatted to deliver HTS of greater than 10⁶ compounds for a specific drug target conclusively demonstrates a new application for stem cells in drug discovery. In the future new molecular entities may be screened directly in human ES or induced pluripotent stem cell derivatives.     

Searching for the most effective screening system to identify cell-active inhibitors of beta-secretase

The beta-secretase BACE1 is an attractive drug target for reducing the level of the Alzheimer's disease-promoting Abeta peptide in the brain. Whereas potent peptidomimetic in vitro inhibitors of BACE1 have been designed, screening approaches to identify cell-permeable small molecule inhibitors have had limited success so far. In the present minireview we summarize existing screening methods, discuss their scope of application in the drug discovery process and compare them to a novel cell-based screening system to identify BACE1 inhibitors by a positive yeast growth selection.