Predicting drug targets based on protein domains

The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.     

A Quantitative High-Resolution Genetic Profile Rapidly Identifies Sequence Determinants of Hepatitis C Viral Fitness and Drug Sensitivity

Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to systematically map the drug-protein interactions between Daclatasvir and NS5A and profile genetic barriers to Daclatasvir resistance. We implemented saturation mutagenesis in combination with next-generation sequencing technology to systematically quantify the effect of every possible amino acid substitution in the drug-targeted region (domain IA of NS5A) on replication fitness and sensitivity to Daclatasvir. This enabled determination of the residues governing drug-protein interactions. The relative fitness and drug sensitivity profiles also provide a comprehensive reference of the genetic barriers for all possible single amino acid changes during viral evolution, which we utilized to predict clinical outcomes using mathematical models. We envision that this high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

     

A computational approach to finding novel targets for existing drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.     

Separation and detection methods for covalent drug-protein adducts

Covalent binding of reactive metabolites of drugs to proteins has been a predominant hypothesis for the mechanism of toxicity caused by numerous drugs. The development of efficient and sensitive analytical methods for the separation, identification, quantification of drug-protein adducts have important clinical and toxicological implications. In the last few decades, continuous progress in analytical methodology has been achieved with substantial increase in the number of new, more specific and more sensitive methods for drug-protein adducts. The methods used for drug-protein adduct studies include those for separation and for subsequent detection and identification. Various chromatographic (e.g., affinity chromatography, ion-exchange chromatography, and high-performance liquid chromatography) and electrophoretic techniques [e.g., sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional SDS-PAGE, and capillary electrophoresis], used alone or in combination, offer an opportunity to purify proteins adducted by reactive drug metabolites. Conventionally, mass spectrometric (MS), nuclear magnetic resonance, and immunological and radioisotope methods are used to detect and identify protein targets for reactive drug metabolites. However, these methods are labor-intensive, and have provided very limited sequence information on the target proteins adducted, and thus the identities of the protein targets are usually unknown. Moreover, the antibody-based methods are limited by the availability, quality, and specificity of antibodies to protein adducts, which greatly hindered the identification of specific protein targets of drugs and their clinical applications. Recently, the use of powerful MS technologies (e.g., matrix-assisted laser desorption/ionization time-of-flight) together with analytical proteomics have enabled one to separate, identify unknown protein adducts, and establish the sequence context of specific adducts by offering the opportunity to search for adducts in proteomes containing a large number of proteins with protein adducts and unmodified proteins. The present review highlights the separation and detection technologies for drug-protein adducts, with an emphasis on methodology, advantages and limitations to these techniques. Furthermore, a brief discussion of the application of these techniques to individual drugs and their target proteins will be outlined.     

Photoaffinity labeling combined with mass spectrometric approaches as a tool for structural proteomics

Protein chemistry, such as crosslinking and photoaffinity labeling, in combination with modern mass spectrometric techniques, can provide information regarding protein-protein interactions beyond that normally obtained from protein identification and characterization studies. While protein crosslinking can make tertiary and quaternary protein structure information available, photoaffinity labeling can be used to obtain structural data about ligand-protein interaction sites, such as oligonucleotide-protein, drug-protein and protein-protein interaction. In this article, we describe mass spectrometry-based photoaffinity labeling methodologies currently used and discuss their current limitations. We also discuss their potential as a common approach to structural proteomics for providing 3D information regarding the binding region, which ultimately will be used for molecular modeling and structure-based drug design.     

A profiling platform for the identification of selective metalloprotease inhibitors

Although proteases represent an estimated 5% to 10% of potential drug targets, inhibitors for metalloproteases (MPs) account for only a small proportion of all approved drugs, failures of which have typically been associated with lack of selectivity. In this study, the authors describe a novel and universal binding assay based on an actinonin derivative and show its binding activities for several MPs and its lack of activity toward all the non-MPs tested. This newly developed assay would allow for the rapid screening for inhibitors of a given MP and for the selectivity profiling of the resulting hits. The assay has successfully enabled for the first time simultaneous profiling of 8 well-known inhibitors against a panel of selected MPs. Previously published activities for these inhibitors were confirmed, and the authors have also discovered new molecular targets for some of them. The authors conclude that their profiling platform provides a generic assay solution for the identification of novel metalloprotease inhibitors as well as their selectivity profiling using a simple and homogeneous assay.     

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.     

Target identification by chromatographic co-elution: monitoring of drug-protein interactions without immobilization or chemical derivatization

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.     

Antimalarial activity of compounds comprising a primary benzene sulfonamide fragment

Despite the urgent need for effective antimalarial drugs with novel modes of action no new chemical class of antimalarial drug has been approved for use since 1996. To address this, we have used a rational approach to investigate compounds comprising the primary benzene sulfonamide fragment as a potential new antimalarial chemotype. We report the in vitro activity against Plasmodium falciparum drug sensitive (3D7) and resistant (Dd2) parasites for a panel of fourteen primary benzene sulfonamide compounds. Our findings provide a platform to support the further evaluation of primary benzene sulfonamides as a new antimalarial chemotype, including the identification of the target of these compounds in the parasite.     

Recent advances in chromatographic and electrophoretic methods for the study of drug-protein interactions

Drug-protein binding is an important process in determining the activity and fate of a pharmaceutical agent once it has entered the body. This review examines various chromatographic and electrophoretic methods that have been developed to study such interactions. An overview of each technique is presented along with a discussion of its strengths, weaknesses and potential applications. Formats that are discussed include the use of both soluble and immobilized drugs or proteins, and approaches based on zonal elution, frontal analysis or vacancy peak measurements. Furthermore, examples are provided that illustrate the use of these methods in determining the overall extent of drug-protein binding, in examining the displacement of a drug by other agents and in measuring the equilibrium or rate constants for drug-protein interactions. Examples are also given demonstrating how the same methods, particularly when used in high-performance liquid chromatography or capillary electrophoresis systems, can be employed as rapid screening tools for investigating the binding of different forms of a chiral drug to a protein or the binding of different proteins and peptides to a given pharmaceutical agent.     

An Integrated Peptide-Antigen Microarray on Plasmonic Gold Films for Sensitive Human Antibody Profiling

High-throughput screening for interactions of peptides with a variety of antibody targets could greatly facilitate proteomic analysis for epitope mapping, enzyme profiling, drug discovery and biomarker identification. Peptide microarrays are suited for such undertaking because of their high-throughput capability. However, existing peptide microarrays lack the sensitivity needed for detecting low abundance proteins or low affinity peptide-protein interactions. This work presents a new peptide microarray platform constructed on nanostructured plasmonic gold substrates capable of metal enhanced NIR fluorescence enhancement (NIR-FE) by hundreds of folds for screening peptide-antibody interactions with ultrahigh sensitivity. Further, an integrated histone peptide and whole antigen array is developed on the same plasmonic gold chip for profiling human antibodies in the sera of systemic lupus erythematosus (SLE) patients, revealing that collectively a panel of biomarkers against unmodified and post-translationally modified histone peptides and several whole antigens allow more accurate differentiation of SLE patients from healthy individuals than profiling biomarkers against peptides or whole antigens alone.     

A Systematic In Silico Search for Target Similarity Identifies Several Approved Drugs with Potential Activity against the Plasmodium falciparum Apicoplast

Most of the drugs in use against Plasmodium falciparum share similar modes of action and, consequently, there is a need to identify alternative potential drug targets. Here, we focus on the apicoplast, a malarial plastid-like organelle of algal source which evolved through secondary endosymbiosis. We undertake a systematic in silico target-based identification approach for detecting drugs already approved for clinical use in humans that may be able to interfere with the P. falciparum apicoplast. The P. falciparum genome database GeneDB was used to compile a list of ≈600 proteins containing apicoplast signal peptides. Each of these proteins was treated as a potential drug target and its predicted sequence was used to interrogate three different freely available databases (Therapeutic Target Database, DrugBank and STITCH3.1) that provide synoptic data on drugs and their primary or putative drug targets. We were able to identify several drugs that are expected to interact with forty-seven (47) peptides predicted to be involved in the biology of the P. falciparum apicoplast. Fifteen (15) of these putative targets are predicted to have affinity to drugs that are already approved for clinical use but have never been evaluated against malaria parasites. We suggest that some of these drugs should be experimentally tested and/or serve as leads for engineering new antimalarials.     

Drug discovery and development focusing on existing medicines: drug re-profiling strategy

As a new strategy for drug discovery and development, I focus on drug re-profiling as a way to identify new treatments for diseases. In this strategy, the actions of existing medicines, whose safety and pharmacokinetic effects in humans have already been confirmed clinically and approved for use, are examined comprehensively at the molecular level and the results used for the development of new medicines. This strategy is based on the fact that we still do not understand the underlying mechanisms of action of many existing medicines, and as such the cellular responses that give rise to their main effects and side effects are yet to be elucidated. To this extent, identification of the mechanisms underlying the side effects of medicines offers a means for us to develop safer drugs. The results can also be used for developing existing drugs for use as medicines for the treatment of other diseases. Promoting this research strategy could provide breakthroughs in drug discovery and development.     

In Silico Repositioning-Chemogenomics Strategy Identifies New Drugs with Potential Activity against Multiple Life Stages of Schistosoma mansoni

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.     

Drug-protein binding studies new trends in analytical and experimental methodology

In the last few years, continuous progress in instrumental analytical methodology has been achieved with a substantial increase in the number of new, more specific and more flexible methods for ligand-protein assays. In general, the methods used for drug-protein binding studies can be divided into two main groups: separation methods (enabling the calculation of binding parameters, i.e. the number of binding sites and their respective affinity constants) and non-separation methods (describing predominantly qualitative parameters of the ligand-protein complex). This review will be focussed particularly on recent trends in the development of drug-protein binding methods including stereoselective and non-stereoselective aspects using chromatography, capillary electrophoresis and microdialysis as compared to the “conventional approach” using equilibrium dialysis, ultrafiltration or size exclusion chromatography. The advantages and limitations of various methods will be discussed including a focus on “optimal” experimental strategies taking into account in vitro, ex vivo and/or in vivo studies. Furthermore, the importance of some particular aspects concerning the drug binding to proteins (covalent binding of drugs and their metabolites, stereoselective interactions and evaluation of binding data) will be outlined in more detail.     

Molecular Modelling of Human DT-Diaphorase For Enzyme-Directed Bioreductive Drug Design

Abstract

The enzyme DT-Diaphorase (NAD(P)H:quinone acceptor oxidoreductase, EC 1.6.99.2.; DTD) has been recognised as a good target for enzyme-directed bioreductive drug development. This is due to elevated levels of enzyme activity in several human tumour types and its role in the bioreductive activation of several quinone-based anti-cancer drugs.

Bioreductive drugs are designed to exploit one of the features of solid tumours, namely tumour hypoxia. However, selectivity of bioreductive drugs is not only governed by oxygen levels, but also by the levels of the enzymes catalysing bioreductive activation, leading to the concept of “enzyme-directed bioreductive drug development” introduced by Workman and Walton in 1990. This concept requires the identification of tumours within a patient that have elevated levels of enzyme activity (enzyme profiling) and treating the patient with drugs activated by such enzymes. DTD has been singled out as a particularly good candidate for such targeting. In order to rationalise the design of drugs to target DTD, molecular modelling techniques have been employed.

The human DTD three-dimensional structure has been modelled with homology to the known rat DTD structure (about 85% identity) and the model refined using energy minimisation. Drug-binding orientations have been determined and molecular dynamics simulations performed. Using data from a series of quinone based compounds with a broad range of substrate specificity we examine drug-enzyme interactions and suggest how DTD substrate specificity might be further optimised.     

Determination of drug-protein binding using supported liquid membrane extraction under equilibrium conditions

A technique for determination of drug-protein binding based on a membrane extraction technique termed "equilibrium sampling through membrane (ESTM)" is presented. It involves the establishment of an equilibrium between an aqueous buffer and either a blood plasma sample or a matched buffer, both containing the drug. Analysis of the aqueous buffer in the two cases gives the drug-protein binding. The principle bypasses some sources of systematic error found with common techniques for this measurement based on e.g. ultrafiltration, as it senses the equilibrium conditions without disturbing the sample. The technique is applied to some local anesthetic drugs as model substances and two alternative ways for the evaluation are presented. Results with these evaluation methods are compared with literature values for the drug-protein binding of these compounds. It is found that the drug-protein binding values obtained are lower than literature values, which is attributed to reduced systematic error.     

Analysis of glioma cell platinum response by metacomparison of two-dimensional chromatographic proteome profiles

Successful clinical development of cancer treatments is aided by the development of molecular markers that allow the identification of patients likely to respond. In the case of broadly cytotoxic drugs, such as the multinuclear series of platinum chemotherapeutic agents that we are evaluating for the treatment of glioma, one route to marker identification is proteomic profiling. We are using the two-dimensional chromatography system, the ProteomeLab PF2D, to compare proteomic profiles of glioma cells in culture before and after drug treatment. The existing software tools allowed the rapid identification of peaks increased by treatment of a given drug as compared with control untreated cells. To compare across these pairs, we developed new software, called the MetaComparison Tool (MCT). The MCT uses the chromatographic characteristics of peaks as identifiers, an approach that was validated by mass spectrometry of two independent isolations of a peak, from cells that were treated with two different platinum compounds. The MCT made it possible to rapidly query whether a given peak responded to more than one treatment and so allowed the identification of peaks that were specific to a given drug. As a result, this analysis greatly reduced the list of peaks whose isolation and downstream analysis by mass spectrometry is warranted, accelerating the search for protein markers of response.