Ultraviolet resonance Raman study of drug binding in dihydrofolate reductase, gyrase, and catechol O-methyltransferase.

This paper presents a study of the use of ultraviolet resonance Raman (UVRR) spectroscopic methods as a means of elucidating aspects of drug-protein interactions. Some of the RR vibrational bands of the aromatic amino acids tyrosine and tryptophan are sensitive to the microenvironment, and the use of UV excitation radiation allows selective enhancement of the spectral features of the aromatic amino acids, enabling observation specifically of their change in microenvironment upon drug binding. The three drug-protein systems investigated in this study are dihydrofolate reductase with its inhibitor trimethoprim, gyrase with novobiocin, and catechol O-methyltransferase with dinitrocatechol. It is demonstrated that UVRR spectroscopy has adequate sensitivity to be a useful means of detecting drug-protein interactions in those systems for which the electronic absorption of the aromatic amino acids changes because of hydrogen bonding and/or possible dipole-dipole and dipole-polarizability interactions with the ligand.     

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.     

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.     

Resistance of proteinuric rats to furosemide: Urinary drug protein binding as a determinant of drug effect

The effect of drug binding to urinary proteins on the diuretic response to furosemide was assessed in normal and nephrotic rats. Nephrosis was induced by treating Sprague-Dawley rats with puromycin aminonucleoside. Binding of furosemide to urinary proteins was found to range from 60 to 95% depending on the concentration of urinary protein. The diuretic response to furosemide reaching the renal tubular lumen was inversely correlated with the degree of proteinuria, a finding that was independent of serum protein concentration of glomerular filtration rate. These data suggest that the binding of furosemide to urinary protein decreases the diuretic effect of furosemide and that drug-protein interactions of this type may also be important in modulating the activity of other lumenally-active drugs or endogenous substances exhibiting a high degree of protein binding. The binding of furosemide to urinary protein may explain the refractoriness of some patients with proteinuria to this agent.     

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.     

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.     

Equilibrium sampling through membrane based on a single hollow fibre for determination of drug-protein binding and free drug concentration in plasma

The determination of drug-protein binding and free drug concentration in plasma applying the equilibrium sampling through membrane (ESTM) technique has been studied using supported liquid membrane extraction in a single hollow fibre without any membrane carrier. In the extraction setup, the donor phase (plasma or buffer) was placed in the vial, into which was immersed the hollow fibre with the acceptor phase situated in the lumen. This proposed technique was applied to study the drug-protein binding of five local anaesthetics and two antidepressants as model substances, and the influence of the total drug concentration on the drug-protein binding was investigated. The brief theoretical background for determination of the drug-protein binding under equilibrium conditions is described. The developed method shows a new, improved and simple procedure for determination of free drug concentration in plasma and extent of drug-protein binding.     

Evidence for a strong selenium-aromatic interaction derived from crystallographic data and ab initio quantum chemical calculations

Increasing attention is being paid to the role of selenium, both as an essential component required for the activity of many enzymes and in the context of selenium-based pharmaceutical agents. A wide range of therapeutics that include selenium are on the market and under development, such as antihypertensive, anticancerogenic, antiviral, and immunosuppressive agents. Computer-aided drug design (CADD) has proven to be an important tool for the development of new drugs. Many CADD techniques, including docking, molecular dynamics simulation, and other receptor-based approaches, require an accurate understanding of the nature of the intermolecular forces that act to stabilize protein-ligand complexes; moreover, a quantitative assessment of these interactions furthers our efforts to rationalize the drug design process. In this paper, we consider one class of interaction involving selenium, that between Se and aromatic rings. Prior work has shown that interactions between divalent sulfur and aromatic rings are observed much more frequently than would be expected on the basis of chance, both in protein structures and the crystal structures of organic compounds that include these moieties. Recent studies on the optimization of inhibitor-protein binding also suggest that sulfur-aromatic interactions are important in stabilizing these complexes and may be crucial focal point for CADD. Given that selenium and sulfur have similar chemistry, and that selenium is significantly more polarizable, we propose that Se-aromatic interactions may also play an important stabilizing role in the structure of folded proteins and in drug-protein complexes. We have tested this hypothesis against data from the Cambridge Crystallographic Database and ab initio quantum chemical calculations. We have found evidence that selenium does interact strongly with aromatic rings and may play a role analogous to sulfur in stabilizing protein folds. In addition, selenium should be considered along with sulfur in rational drug design strategies that seek to improve binding to target protein sites that include aromatic rings.     

Survey of phosphorylation near drug binding sites in the Protein Data Bank (PDB) and their effects

While it is currently estimated that 40 to 50% of eukaryotic proteins are phosphorylated, little is known about the frequency and local effects of phosphorylation near pharmaceutical inhibitor binding sites. In this study, we investigated how frequently phosphorylation may affect the binding of drug inhibitors to target proteins. We examined the 453 non‐redundant structures of soluble mammalian drug target proteins bound to inhibitors currently available in the Protein Data Bank (PDB). We cross‐referenced these structures with phosphorylation data available from the PhosphoSitePlus database. Three hundred twenty‐two of 453 (71%) of drug targets have evidence of phosphorylation that has been validated by multiple methods or labs. For 132 of 453 (29%) of those, the phosphorylation site is within 12 Å of the small molecule‐binding site, where it would likely alter small molecule binding affinity. We propose a framework for distinguishing between drug‐phosphorylation site interactions that are likely to alter the efficacy of drugs versus those that are not. In addition we highlight examples of well‐established drug targets, such as estrogen receptor alpha, for which phosphorylation may affect drug affinity and clinical efficacy. Our data suggest that phosphorylation may affect drug binding and efficacy for a significant fraction of drug target proteins. Proteins 2015; 83:25–36. © 2014 Wiley Periodicals, Inc.     

Recent advances in enantiomer separations by affinity capillary electrophoresis using proteins and peptides

Enantiomer separations by capillary electrophoresis (CE), using proteins as chiral selectors--affinity capillary electrophoresis (ACE) with free solutions and capillary electrochromatography (CEC)--with protein immobilized capillaries, are reviewed. The separation principle, recent advances in this field and some interesting topics are presented. In ACE, various enantiomer separations have been already reported using either plasma proteins or egg white ones. Miscellaneous proteins were also explored in the last few years. On the contrary, only a limited number of enantiomer separations have been successfully achieved in CEC. CEC is not yet mature enough to date, and further investigations, such as efficiency, durability and reproducibility of capillaries, will be necessary for the use of routine analyses. The study of enantioselective drug-protein binding is important in pharmaceutical developments. Some applications including high-performance CE/frontal analysis (HPCE/FA) are introduced in this paper.     

Mass spectrometry approaches to monitor protein-drug interactions

Recent advances in mass spectrometry-based approaches have enabled the investigation of drug-protein interactions in various ways including the direct detection of drug-target complexes, the examination of drug-induced changes in the target protein structure, and the monitoring of enzymatic target activity. Mass spectrometry-based proteomics methods also permit the unbiased analysis of changes in protein abundance and post-translational modifications induced by drug action. Finally, chemoproteomic affinity enrichment studies enable the deconvolution of drug targets under close to physiological conditions. This review provides an overview of current methods for the characterization of drug-target interactions by mass spectrometry and describes a protocol for chemoproteomic target binding studies using immobilized bioactive molecules.     

Network-based method to infer the contributions of proteins to the etiology of drug side effects

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug-target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation---maximization algorithm and a diffusion kernel-based approach to estimate each protein’s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii Lei-Gong-Teng. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.     

Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor

MOTIVATION: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.     

Hypothyroidism Side Effect in Patients Treated with Sunitinib or Sorafenib: Clinical and Structural Analyses

Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.     

A new strategy for improved secondary screening and lead optimization using high-resolution SPR characterization of compound-target interactions

Biophysical label-free assays such as those based on SPR are essential tools in generating high-quality data on affinity, kinetic, mechanistic and thermodynamic aspects of interactions between target proteins and potential drug candidates. Here we show examples of the integration of SPR with bioinformatic approaches and mutation studies in the early drug discovery process. We call this combination 'structure-based biophysical analysis'. Binding sites are identified on target proteins using information that is either extracted from three-dimensional structural analysis (X-ray crystallography or NMR), or derived from a pharmacore model based on known binders. The binding site information is used for in silico screening of a large substance library (e.g. available chemical directory), providing virtual hits. The three-dimensional structure is also used for the design of mutants where the binding site has been impaired. The wild-type target and the impaired mutant are then immobilized on different spots of the sensor chip and the interactions of compounds with the wild-type and mutant are compared in order to identify selective binders for the binding site of the target protein. This method can be used as a cost-effective alternative to high-throughput screening methods in cases when detailed binding site information is available. Here, we present three examples of how this technique can be applied to provide invaluable data during different phases of the drug discovery process.     

Protein‐spanning water networks and implications for prediction of protein–protein interactions mediated through hydrophobic effects

Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self‐assembly processes. Protein–protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein–protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy‐based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue‐based water density and single‐linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces. Proteins 2014; 82:3312–3326. © 2014 Wiley Periodicals, Inc.     

Chemical proteomics from a nuclear magnetic resonance spectroscopy perspective

Proteomics is the study of the protein complement of a genome and employs a number of newly emerging tools. One such tool is chemical proteomics, which is a branch of proteomics devoted to the exploration of protein function using both in vitro and in vivo chemical probes. Chemical proteomics aims to define protein function and mechanism at the level of directly observed protein–ligand interactions, whereas chemical genomics aims to define the biological role of a protein using chemical knockouts and observing phenotypic changes. Chemical proteomics is therefore traditional mechanistic biochemistry performed in a systems-based manner, using either activity- or affinity-based probes that target proteins related by chemical reactivities or by binding site shape/properties, respectively. Systems are groups of proteins related by metabolic pathway, regulatory pathway or binding to the same ligand. Studies can be based on two main types of proteome samples: pooled proteins (1 mixture of N proteins) or isolated proteins in a given system and studied in parallel (N single protein samples). Although the field of chemical proteomics originated with the use of covalent labeling strategies such as isotope-coded affinity tagging, it is expanding to include chemical probes that bind proteins noncovalently, and to include more methods for observing protein–ligand interactions. This review presents an emerging role for nuclear magnetic resonance spectroscopy in chemical proteomics, both in vitro and in vivo. Applications include: functional proteomics using cofactor fingerprinting to assign proteins to gene families; gene family-based structural characterizations of protein–ligand complexes; gene family-focused design of drug leads; and chemical proteomic probes using nuclear magnetic resonance SOLVE and studies of protein–ligand interactions in vivo.     

Discovery of receptor-ligand interfaces in the immunoglobulin superfamily

Cell-surface-anchored immunoglobulin superfamily (IgSF) proteins are widespread throughout the human proteome, forming crucial components of diverse biological processes including immunity, cell-cell adhesion, and carcinogenesis. IgSF proteins generally function through protein-protein interactions carried out between extracellular, membrane-bound proteins on adjacent cells, known as trans-binding interfaces. These protein-protein interactions constitute a class of pharmaceutical targets important in the treatment of autoimmune diseases, chronic infections, and cancer. A molecular-level understanding of IgSF protein-protein interactions would greatly benefit further drug development. A critical step toward this goal is the reliable identification of IgSF trans-binding interfaces. We propose a novel combination of structure and sequence information to identify trans-binding interfaces in IgSF proteins. We developed a structure-based binding interface prediction approach that can identify broad regions of the protein surface that encompass the binding interfaces and suggests that IgSF proteins possess binding supersites. These interfaces could theoretically be pinpointed using sequence-based conservation analysis, with performance approaching the theoretical upper limit of binding interface prediction accuracy, but achieving this in practice is limited by the current ability to identify an appropriate multiple sequence alignment for conservation analysis. However, an important contribution of combining the two orthogonal methods is that agreement between these approaches can estimate the reliability of the predictions. This approach was benchmarked on the set of 22 IgSF proteins with experimentally solved structures in complex with their ligands. Additionally, we provide structure-based predictions and reliability scores for the 62 IgSF proteins with known structure but yet uncharacterized binding interfaces.     

Chemical proteomics from a nuclear magnetic resonance spectroscopy perspective

Proteomics is the study of the protein complement of a genome and employs a number of newly emerging tools. One such tool is chemical proteomics, which is a branch of proteomics devoted to the exploration of protein function using both in vitro and in vivo chemical probes. Chemical proteomics aims to define protein function and mechanism at the level of directly observed protein-ligand interactions, whereas chemical genomics aims to define the biological role of a protein using chemical knockouts and observing phenotypic changes. Chemical proteomics is therefore traditional mechanistic biochemistry performed in a systems-based manner, using either activity- or affinity-based probes that target proteins related by chemical reactivities or by binding site shape/properties, respectively. Systems are groups of proteins related by metabolic pathway, regulatory pathway or binding to the same ligand. Studies can be based on two main types of proteome samples: pooled proteins (1 mixture of N proteins) or isolated proteins in a given system and studied in parallel (N single protein samples). Although the field of chemical proteomics originated with the use of covalent labeling strategies such as isotope-coded affinity tagging, it is expanding to include chemical probes that bind proteins noncovalently, and to include more methods for observing protein-ligand interactions. This review presents an emerging role for nuclear magnetic resonance spectroscopy in chemical proteomics, both in vitro and in vivo. Applications include: functional proteomics using cofactor fingerprinting to assign proteins to gene families; gene family-based structural characterizations of protein-ligand complexes; gene family-focused design of drug leads; and chemical proteomic probes using nuclear magnetic resonance SOLVE and studies of protein-ligand interactions in vivo.     

Similar binding sites and different partners: implications to shared proteins in cellular pathways

We studied a data set of structurally similar interfaces that bind to proteins with different binding-site structures and different functions. Our multipartner protein interface clusters enable us to address questions like: What makes a given site bind different proteins? How similar/different are the interactions? And, what drives the apparently less-specific association? We find that proteins with common binding-site motifs preferentially use conserved interactions at similar interface locations, despite the different partners. Helices are major vehicles for binding different partners, allowing alternate ways to achieve favorable association. The binding sites are characterized by imperfect packing, planar architectures, bridging water molecules, and, on average, smaller size. Interestingly, analysis of the connectivity of these proteins illustrates that they have more interactions with other proteins. These findings are important in predicting "date hubs," if we assume that "date hubs" are shared proteins with binding sites capable of transient binding to multipartners, linking higher-order networks.