Binding-Site Assessment by Virtual Fragment Screening

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors.     

Cracking the molecular weight barrier: fragment screening of an aminotransferase using an NMR-based functional assay

NMR-based screening of protein targets has become a well established part of the drug discovery process especially with respect to fragments. However, as target size increases the two-dimensional spectra typically used for such screening become more crowded due to the increased number of signals, and the individual signals broaden due to the decreased rotational correlation time of the protein. Here we present an NMR-based functional assay for the branched-chain aminotransferase BCATc, a dimer with a total molecular weight of 88 kDa, which overcomes the limitations of the typical protein-based NMR screening method. BCATc is involved in glutamate production in the brain and is a therapeutic target for neuronal disorders involving a glutamatergic mechanism. Several fragments which inhibit BCATc were discovered using this assay and these may serve as novel cores for the development of potent BCATc inhibitors.     

Fluorine nuclear magnetic resonance-based assay in living mammalian cells

Nuclear magnetic resonance (NMR)-based screening has been recognized as a powerful approach for the identification and characterization of molecules interacting with pharmaceutical targets. Indeed, several NMR methods have been developed and successfully applied to many drug discovery projects. Whereas most of these approaches have targeted isolated biomolecular receptors, very few cases are reported with the screening performed in intact cells and cell extracts. Here we report the first successful application of the fluorine NMR-based assay n-FABS (n-fluorine atoms for biochemical screening) in living mammalian cells expressing the membrane protein fatty acid amide hydrolase (FAAH). This method allows the identification of both weak and potent inhibitors and the measurement of their potency in a physiological environment.     

Determining the optimal size of small molecule mixtures for high throughput NMR screening

High-throughput screening (HTS) using NMR spectroscopy has become a common component of the drug discovery effort and is widely used throughout the pharmaceutical industry. NMR provides additional information about the nature of small molecule-protein interactions compared to traditional HTS methods. In order to achieve comparable efficiency, small molecules are often screened as mixtures in NMR-based assays. Nevertheless, an analysis of the efficiency of mixtures and a corresponding determination of the optimum mixture size (OMS) that minimizes the amount of material and instrumentation time required for an NMR screen has been lacking. A model for calculating OMS based on the application of the hypergeometric distribution function to determine the probability of a hit for various mixture sizes and hit rates is presented. An alternative method for the deconvolution of large screening mixtures is also discussed. These methods have been applied in a high-throughput NMR screening assay using a small, directed library.     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

Identification of B. anthracis N5-carboxyaminoimidazole ribonucleotide mutase (PurE) active site binding compounds via fragment library screening

The de novo purine biosynthesis pathway is an attractive target for antibacterial drug design, and PurE from this pathway has been identified to be crucial for Bacillus anthracis survival in serum. In this study we adopted a fragment-based hit discovery approach, using three screening methods—saturation transfer difference nucleus magnetic resonance (STD-NMR), water-ligand observed via gradient spectroscopy (WaterLOGSY) NMR, and surface plasmon resonance (SPR), against B. anthracis PurE (BaPurE) to identify active site binding fragments by initially testing 352 compounds in a Zenobia fragment library. Competition STD NMR with the BaPurE product effectively eliminated non-active site binding hits from the primary hits, selecting active site binders only. Binding affinities (dissociation constant, K_D) of these compounds varied between 234 and 301 μM. Based on test results from the Zenobia compounds, we subsequently developed and applied a streamlined fragment screening strategy to screen a much larger library consisting of 3000 computationally pre-selected fragments. Thirteen final fragment hits were confirmed to exhibit binding affinities varying from 14 μM to 700 μM, which were categorized into five different basic scaffolds. All thirteen fragment hits have ligand efficiencies higher than 0.30. We demonstrated that at least two fragments from two different scaffolds exhibit inhibitory activity against the BaPurE enzyme.     

Recommended strategies for spectral processing and post-processing of 1D <Superscript>1</Superscript>H-NMR data of biofluids with a particular focus on urine

¹H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.     

A strategy for high-throughput assay development using leads derived from nuclear magnetic resonance-based screening

A strategy is described for the development of high-throughput screening assays against targets of unknown function that involves the use of nuclear magnetic resonance (NMR) spectroscopy. Using this approach, molecules that bind to the protein target are identified from an NMR-based screen of a library of substrates, cofactors, and other compounds that are known to bind to many proteins and enzymes. Once a ligand has been discovered, a fluorescent or radiolabeled analog of the ligand is synthesized that can be used in a high-throughput screen. The approach is illustrated in the development of a high-throughput screening assay against HI-0033, a conserved protein from Haemophilus influenzae whose function is currently unknown. Adenosine was found to bind to HI-0033 by NMR, and fluorescent analogs were rapidly identified that bound to HI-0033 in the submicromolar range. Using these fluorescent compounds, a fluorescence polarization assay was developed that is suitable for high-throughput screening and obtaining detailed structure-activity relationships for lead optimization.     

Discovery of novel inhibitors of the ZipA/FtsZ complex by NMR fragment screening coupled with structure-based design

ZipA is a membrane anchored protein in Escherichia coli that interacts with FtsZ, a homolog of eukaryotic tubulins, forming a septal ring structure that mediates bacterial cell division. Thus, the ZipA/FtsZ protein-protein interaction is a potential target for an antibacterial agent. We report here an NMR-based fragment screening approach which identified several hits that bind to the C-terminal region of ZipA. The screen was performed by 1H-15N HSQC experiments on a library of 825 fragments that are small, lead-like, and highly soluble. Seven hits were identified, and the binding mode of the best one was revealed in the X-ray crystal structure. Similar to the ZipA/FtsZ contacts, the driving force in the binding of the small molecule ligands to ZipA is achieved through hydrophobic interactions. Analogs of this hit were also evaluated by NMR and X-ray crystal structures of these analogs with ZipA were obtained, providing structural information to help guide the medicinal chemistry efforts.     

Optimizing restriction fragment fingerprinting methods for ordering large genomic libraries

We present a statistical analysis of the problem of ordering large genomic cloned libraries through overlap detection based on restriction fingerprinting. Such ordering projects involve a large investment of effort involving many repetitious experiments. Our primary purpose here is to provide methods of maximizing the efficiency of such efforts. To this end, we adopt a statistical approach that uses the likelihood ratio as a statistic to detect overlap. The main advantages of this approach are that (1) it allows the relatively straightforward incorporation of the observed statistical properties of the data; (2) it permits the efficiency of a particular experimental method for detecting overlap to be quantitatively defined so that alternative experimental designs may be compared and optimized; and (3) it yields a direct estimate of the probability that any two library members overlap. This estimate is a critical tool for the accurate, automatic assembly of overlapping sets of fragments into islands called "contigs." These contigs must subsequently be connected by other methods to provide an ordered set of overlapping fragments covering the entire genome.     

Expression, refolding, and isotopic labeling of human serum albumin domains for NMR spectroscopy

Many different compounds bind to human serum albumin (HSA), which can be a significant problem in the drug discovery process. To aid in the design of drug molecules that do not bind to HSA, the structures of HSA/ligand complexes would be very useful. However, little information has been reported on the structures of small molecules complexed to HSA. In this paper, we describe a procedure for preparing isotopically labeled domains of HSA for nuclear magnetic resonance (NMR) studies. The procedure involves the expression in Escherichia coli, refolding, and a multistep purification. Domains I and III are capable of folding into stable structural units and producing well resolved (15)N/(1)H correlation spectra, whereas domain II forms significant aggregates at sub-millimolar concentration. Using our protocols, isotopically labeled and properly folded domains I and III can be effectively produced in large quantities for NMR-based structural studies and NMR-based screening. This provides a valuable tool for obtaining structural information on HSA/ligand complexes by NMR which will be useful in drug discovery.     

Protein structural class identification directly from NMR spectra using averaged chemical shifts

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.     

NMR-based metabonomic study of Chinese medicine Gegen Qinlian Decoction as an effective treatment for type 2 diabetes in rats

Metabonomic profiles of the type 2 diabetic rats induced by streptozotocin and high-sugar, high fat diet on the treatment of Gegen Qinlian Decoction (GQD) for 9 weeks were investigated. Rats were randomly divided into five groups: normal control (NC), type 2 diabetes (DM), metformin hydrochloric, GQD in high and low dosages. Plasma samples for ¹H NMR-based metabolomic research, serum samples for clinical biochemistry, and liver and pancreas tissues for histopathology test were collected. Compared with NC rats, metabolic pathways of DM rats were revealed to be altered by pattern analyses of plasma NMR data, which was further correlated with serum biochemistry. Cross-validated scores mean trajectory derived from PLS-DA of NMR spectra demonstrated that GQD significantly restored the abnormal metabolic state in the long run, more potent than metformin hydrochloric. Detailed analysis of the altered metabolite levels indicated that GQD significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism, amino acid metabolism and gut microbial metabolism and N-acetyl group metabolism. The results confirmed the hypoglycemic efficacy of GQD and its ability to ameliorate the diabetic symptoms in a global scale. NMR-based metabonomics approach is helpful for the further understanding of diabetes-related mechanisms.     

<Superscript>1</Superscript>H NMR-based metabolomics combined with HPLC-PDA-MS-SPE-NMR for investigation of standardized <Emphasis Type="Italic">Ginkgo biloba</Emphasis> preparations

Commercial preparations of Ginkgo biloba are very complex mixtures prepared from raw leaf extracts by a series of extraction and prepurification steps. The pharmacological activity is attributed to a number of flavonoid glycosides and unique terpene trilactones (TTLs), with largely uncharacterized pharmacological profiles on targets involved in neurological disorders. It is therefore important to complement existing targeted analytical methods for analysis of Ginkgo biloba preparations with alternative technology platforms for their comprehensive and global characterization. In this work, ¹H NMR-based metabolomics and hyphenation of high-performance liquid chromatography, photo-diode array detection, mass spectrometry, solid-phase extraction, and nuclear magnetic resonance spectroscopy (HPLC-PDA-MS-SPE-NMR) were used for investigation of 16 commercially available preparations of Ginkgo biloba. The standardized extracts originated from Denmark, Italy, Sweden, and United Kingdom, and the results show that ¹H NMR spectra allow simultaneous assessment of the content as well as identity of flavonoid glycosides and TTLs based on a very simple sample-preparation procedure consisting of extraction, evaporation and reconstitution in acetone-d ₆. Unexpected or unwanted extract constituents were also easily identified in the ¹H NMR spectra, which contrasts traditional methods that depend on UV absorption or MS ionizability and usually require availability of reference standards. Automated integration of ¹H NMR spectral segments (buckets or bins of 0.02 ppm width) provides relative distribution plots of TTLs based on their H-12 resonances. The present study shows that ¹H NMR-based metabolomics is an attractive method for non-selective and comprehensive analysis of Ginkgo extracts.     

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.     

NMR tools for biotechnology

Recent developments in NMR spectroscopy verify that NMR continues to be an exciting area of research. These advances can be placed into three general categories: new hardware; new techniques; and novel applications. The hardware developments include many advances in the area of flow NMR and some new probe designs. The new techniques include several ways to edit the NMR spectra of mixtures without using chromatographic separation. These new NMR tools are now allowing us to analyze complex mixtures, combinatorial-chemistry libraries, bound drugs, unstable compounds, very small samples, and heterogeneous samples.