Integration of NMR and high-throughput screening

NMR-based screening has become a powerful method for the identification and analysis of low-molecular weight organic compounds that bind to protein targets and can be utilized in drug discovery programs. In particular, heteronuclear NMR-based screening can yield information about both the affinity and binding location of potential lead compounds. In addition, heteronuclear NMR-based screening has wide applications in complementing and facilitating conventional high-throughout screening programs. This article will describe several strategies for the integration of NMR-based screening and high-throughput screening. The marriage of these two techniques promises to be of tremendous benefit in the triage of hits that come from HTS, and can aid the medicinal chemist in the identification of quality leads that have high potential for further optimization.     

Estimating protein-ligand binding affinity using high-throughput screening by NMR

Many of today's drug discovery programs use high-throughput screening methods that rely on quick evaluations of protein activity to rank potential chemical leads. By monitoring biologically relevant protein-ligand interactions, NMR can provide a means to validate these discovery leads and to optimize the drug discovery process. NMR-based screens typically use a change in chemical shift or line width to detect a protein-ligand interaction. However, the relatively low throughput of current NMR screens and their high demand on sample requirements generally makes it impractical to collect complete binding curves to measure the affinity for each compound in a large and diverse chemical library. As a result, NMR ligand screens are typically limited to identifying candidates that bind to a protein and do not give any estimate of the binding affinity. To address this issue, a methodology has been developed to rank binding affinities for ligands based on NMR screens that use 1D (1)H NMR line-broadening experiments. This method was demonstrated by using it to estimate the dissociation equilibrium constants for twelve ligands with the protein human serum albumin (HSA). The results were found to give good agreement with previous affinities that have been reported for these same ligands with HSA.     

RAMPED-UP NMR: multiplexed NMR-based screening for drug discovery

The crucial step in drug discovery is the identification of a lead compound from a vast chemical library by any number of screening techniques. NMR-based screening has the advantage of directly detecting binding of a compound to the target. The spectra resulting from these screens can also be very complex and difficult to analyze, making this an inefficient process. We present here a method, RAMPED-UP NMR, (Rapid Analysis and Multiplexing of Experimentally Discriminated Uniquely Labeled Proteins using NMR) which generates simple spectra which are easy to interpret and allows several proteins to be screened simultaneously. In this method, the proteins to be screened are uniquely labeled with one amino acid type. There are several benefits derived from this unique labeling strategy: the spectra are greatly simplified, resonances that are most likely to be affected by binding are the only ones observed, and peaks that yield little or no information upon binding are eliminated, allowing the analysis of multiple proteins easily and simultaneously. We demonstrate the ability of three different proteins to be analyzed simultaneously for binding to two different ligands. This method will have significant impact in the use of NMR spectroscopy for both the lead generation and lead optimization phases of drug discovery by its ability to increase screening throughput and the ability to examine selectivity. To the best of our knowledge, this is the first time in any format that multiple proteins can be screened in one tube.     

ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens

High-throughput screening (HTS) of large compound collections typically results in numerous small molecule hits that must be carefully evaluated to identify valid drug leads. Although several filtering mechanisms and other tools exist that can assist the chemist in this process, it is often the case that costly synthetic resources are expended in pursuing false positives. We report here a rapid and reliable NMR-based method for identifying reactive false positives including those that oxidize or alkylate a protein target. Importantly, the reactive species need not be the parent compound, as both reactive impurities and breakdown products can be detected. The assay is called ALARM NMR (a La assay to detect reactive molecules by nuclear magnetic resonance) and is based on monitoring DTT-dependent (13)C chemical shift changes of the human La antigen in the presence of a test compound or mixture. Extensive validation has been performed to demonstrate the reliability and utility of using ALARM NMR to assess thiol reactivity. This included comparing ALARM NMR to a glutathione-based fluorescence assay, as well as testing a collection of more than 3500 compounds containing HTS hits from 23 drug targets. The data show that current in silico filtering tools fail to identify more than half of the compounds that can act via reactive mechanisms. Significantly, we show how ALARM NMR data has been critical in identifying reactive compounds that would otherwise have been prioritized for lead optimization. In addition, a new filtering tool has been developed on the basis of the ALARM NMR data that can augment current in silico programs for identifying nuisance compounds and improving the process of hit triage.     

Improving on nature's defenses: optimization & high throughput screening of antimicrobial peptides

Antimicrobial peptides (AMPs) are ubiquitous in nature where they play important roles in host defense and microbial control. Despite their natural origin, antimicrobial spectrum and potency, the lead peptide candidates that so far have entered pharmaceutical development have all been further optimized by rational or semi-rational approaches. In recent years, several high throughput screening (HTS) systems have been developed to specifically address optimization of AMPs. These include a range of computational in silico systems and cell-based in vivo systems. The in silico-based screening systems comprise several computational methods such as Quantitative Structure/Activity Relationships (QSAR) as well as simulation methods mimicking peptide/membrane interactions. The in vivo-based systems can be divided in cis-acting and trans-acting screening systems. The cis-acting pre-screens, where the AMP exerts its antimicrobial effect on the producing cell, allow screening of millions or even billions of lead candidates for their basic antimicrobial or membrane-perturbating activity. The trans-acting screens, where the AMP is secreted or actively liberated from the producing cell and interacts with cells different from the producing cell, allow for screening under more complex and application-relevant conditions. This review describes the application of HTS systems employed for AMPs and lists advantages as well as limitations of these systems.     

Design and characterization of libraries of molecular fragments for use in NMR screening against protein targets

We have designed four generations of a low molecular weight fragment library for use in NMR-based screening against protein targets. The library initially contained 723 fragments which were selected manually from the Available Chemicals Directory. A series of in silico filters and property calculations were developed to automate the selection process, allowing a larger database of 1.79 M available compounds to be searched for a further 357 compounds that were added to the library. A kinase binding pharmacophore was then derived to select 174 kinase-focused fragments. Finally, an additional 61 fragments were selected to increase the number of different pharmacophores represented within the library. All of the fragments added to the library passed quality checks to ensure they were suitable for the screening protocol, with appropriate solubility, purity, chemical stability, and unambiguous NMR spectrum. The successive generations of libraries have been characterized through analysis of structural properties (molecular weight, lipophilicity, polar surface area, number of rotatable bonds, and hydrogen-bonding potential) and by analyzing their pharmacophoric complexity. These calculations have been used to compare the fragment libraries with a drug-like reference set of compounds and a set of molecules that bind to protein active sites. In addition, an analysis of the overall results of screening the library against the ATP binding site of two protein targets (HSP90 and CDK2) reveals different patterns of fragment binding, demonstrating that the approach can find selective compounds that discriminate between related binding sites.     

Discovery of novel targets with high throughput RNA interference screening

High throughput technologies have the potential to affect all aspects of drug discovery. Considerable attention is paid to high throughput screening (HTS) for small molecule lead compounds. The identification of the targets that enter those HTS campaigns had been driven by basic research until the advent of genomics level data acquisition such as sequencing and gene expression microarrays. Large-scale profiling approaches (e.g., microarrays, protein analysis by mass spectrometry, and metabolite profiling) can yield vast quantities of data and important information. However, these approaches usually require painstaking in silico analysis and low-throughput basic wet-lab research to identify the function of a gene and validate the gene product as a potential therapeutic drug target. Functional genomic screening offers the promise of direct identification of genes involved in phenotypes of interest. In this review, RNA interference (RNAi) mediated loss-of-function screens will be discussed and as well as their utility in target identification. Some of the genes identified in these screens should produce similar phenotypes if their gene products are antagonized with drugs. With a carefully chosen phenotype, an understanding of the biology of RNAi and appreciation of the limitations of RNAi screening, there is great potential for the discovery of new drug targets.     

Application of shape-based and pharmacophore-based in silico screens for identification of Type II protein kinase inhibitors

The identification of new, potent and selective inhibitors of important protein kinase targets is a major goal of drug discovery. Here we analyze the crystal structures of 55 protein kinase complexes with Type II inhibitors and find they adopt a conserved twisted V-shape, with an angle of 121?±?8° and twist of 78?±?8°. The tightly conserved twist appears important in ensuring ligands curve around the protein backbone and towards the deep pocket. From this, we develop predictive pharmacophore- and shape-based screens to identify Type II inhibitors from a database which also contains Type I inhibitors as decoys. Both approaches exhibit a good level of discrimination for Type II molecules. The most effective pharmacophore model requires six features and three excluded volume regions. Shape-based screening using ROCS generally performs at least as well as pharmacophore approaches. There is only a moderate dependence of shape-based or pharmacophore-based screens on the underlying conformer generator (MOE, Macromodel, Omega and SPE), as well as on ligand linkage chemistry (amide and urea). Finally, we apply our approach to retrieval of Type II inhibitors from a modified version of the DUD database, containing over 104,000 compounds. We observe good enrichment, providing further evidence that the in silico screens developed here will constitute useful guides for identification of small molecule inhibitors targetting protein kinases in their inactive conformational state.     

Fast 2D NMR ligand screening using Hadamard spectroscopy

Fast 2D NMR-based screening can be achieved using Hadamard encoded spectroscopy to focus on the signals of interest (e.g., enzyme active or ligand recognition sites). By recording a set of Hadamard spectra (a "Hadamard constellation") with relative offsets comparable to the excitation bandwidth, quantitative ligand-induced shifts can be obtained from peak intensities.     

Towards the development of synthetic routes using theoretical calculations: an application of in silico screening to 2,6-dimethylchroman-4-one

This study describes an attempt to develop a synthetic route using theoretical calculations, i.e., in silico synthesis route development. The KOSP program created four potential synthetic routes for generating 2,6-dimethylchroman-4-one. In silico screening of these four synthetic routes was then performed. In silico screening involves theoretical analysis of synthetic routes prior to actual experimental work. A synthetic route using the Mitsunobu reaction had already been reported by Hoddgets et al. Theoretical investigations were also conducted on two S(N)Ar reactions as well as a Michael reaction before they were examined experimentally. In silico screening using DFT calculations indicated that only the Michael reaction was likely to produce the target. Experimental work confirmed that the target was obtained in a yield of 76.4% using the Michael reaction. The other two routes, except for the Mitsunobu reaction, failed to generate the target. Our results demonstrate that theoretical calculations can be used to narrow down the number of experiments that need to be conducted when developing novel synthetic routes.     

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.     

Discovery of a potent,selective protein tyrosine phosphatase 1B inhibitor using a linked-fragment strategy

Protein tyrosine phosphatase 1B (PTP1B) is an enzyme that downregulates the insulin receptor. Inhibition of PTP1B is expected to improve insulin action, and the design of small molecule PTP1B inhibitors to treat type II diabetes has received considerable attention. In this work, NMR-based screening identified a nonselective competitive inhibitor of PTP1B. A second site ligand was also identified by NMR-based screening and then linked to the catalytic site ligand by rational design. X-ray data confirmed that the inhibitor bound with the catalytic site in the native, "open" conformation. The final compound displayed excellent potency and good selectivity over many other phosphatases. The modular approach to drug design described in this work should be applicable for the design of potent and selective inhibitors of other therapeutically relevant protein tyrosine phosphatases.     

Phenotypic screening of small molecule libraries by high throughput cell imaging

We have developed high throughput fluorescence cell imaging methods to screen chemical libraries for compounds with effects on diverse aspects of cell physiology. We describe screens for compounds that arrest cells in mitosis, that block cell migration, and that block the secretory pathway. Each of these screens yielded specific inhibitors for research use, and the mitosis screen identified Eg5 as a potential target protein for cancer chemotherapy. Cell imaging provides a large amount of information from primary screening data that can be used to distinguish compounds with different effects on cells, and together with automated analysis, to quantitate compound effects.     

Electrode chemistry yields a nanoparticle-based NMR sensor for calcium

Magnetic nanoparticles (NPs) have been used to obtain NMR-based sensors for analytes ranging from small molecules to viruses by the conjugation of biomolecules (antibodies, proteins, oligonucleotides) to the surface of NPs. In the presence of an analyte, the NPs form clusters that alter the relaxation time of the surrounding water protons. Here, we show that an organic molecule that binds calcium ions of nonbiological origin, rather than a biomolecule, can be employed to modify the surface of a magnetic NP. When calcium ions are added, they induce NP clustering, providing an NMR-based sensor for these ions. Our work suggests that the many chemistries of nonbiological origin, such as those employed for ion-selective electrodes, can be adapted to obtain NMR-based sensors for ions.     

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less‐active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high‐throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high‐throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.     

Discovery of aminoglycoside mimetics by NMR-based screening of Escherichia coli A-site RNA

A method is described for the NMR-based screening for the discovery of aminoglycoside mimetics that bind to Escherichia coli A-site RNA. Although aminoglycosides are clinically useful, they exhibit high nephrotoxicity and ototoxicity, and their overuse has led to the development of resistance to important microbial pathogens. To identify a new series of aminoglycoside mimetics that could potentially overcome the problems associated with toxicities and resistance development observed with the aminoglycosides, we have prepared large quantities of E. coli 16 S A-site RNA and conducted an NMR-based screening of our compound library in search for small-molecule RNA binders against this RNA target. From these studies, several classes of compounds were identified as initial hits with binding affinities in the range of 70 microM to 3 mM. Lead optimization through synthetic modifications of these initial hits led to the discovery of several small-molecule aminoglycoside mimetics that are structurally very different from the known aminoglycosides. Structural models of the A-site RNA/ligand complexes were prepared and compared to the three-dimensional structures of the RNA/aminoglycoside complexes.     

DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions

For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). This automated process enables creating tailor-made decoy sets for any given sets of bioactives. It facilitates a target-dependent validation of docking algorithms and scoring functions helping to save time and resources. We have developed metrics for assessing and improving decoy set quality and employ them to investigate how decoy embedding affects docking. We demonstrate that screening performance is target-dependent and can be impaired by latent actives in the decoy set (LADS) or enhanced by poor decoy embedding. The presented method allows extending and complementing the collection of publicly available high quality decoy sets toward new target space. All present and future DEKOIS data sets will be made accessible at www.dekois.com.     

2-Fluoro-ATP as a versatile tool for 19F NMR-based activity screening

19F NMR-based methods have found utility in activity-based screening assays. However, because enzymes catalyze a diverse set of reactions, a large variety of fluorinated substrates would need to be identified to target each one separately. We have developed a more streamlined approach that is applicable to many enzymes that utilize ATP as a substrate. In this method, a fluorine-containing ATP analogue, 2-fluoro-ATP, is used to monitor the reaction. Applications are described for nicotinamide adenine dinucleotide synthetase and 3-phosphoinositide dependent kinase-1. Fragment screening results for the latter indicate that this technique can identify compounds that inhibit as well as activate reactions. The present results, together with previous biochemical studies from other laboratories, have shown that 2-fluoro-ATP can serve as a substrate for nine enzymes that are representative of three of the six enzyme subclasses, namely the transferases, hydrolases, and ligases. This suggests that 2-fluoro-ATP is suitable as a universal tool for screening ATP-requiring enzymes. Importantly, 2-fluoro-ATP has been determined to be a valid substrate for a variety of kinases, including both small molecule and protein kinases, suggesting that it may be useful for investigating the large number of pharmaceutically relevant kinases.     

High throughput screening for neurodegeneration and complex disease phenotypes

High throughput screening (HTS) for complex diseases is challenging. This stems from the fact that complex phenotypes are difficult to adapt to rapid, high throughput assays. We describe the recent development of high throughput and high-content screens (HCS) for neurodegenerative diseases, with a focus on inherited neurodegenerative disorders, such as Huntington's disease. We describe, among others, HTS assays based on protein aggregation, neuronal death, caspase activation and mutant protein clearance. Furthermore, we describe high-content screens that are being used to prioritize hits identified in such HTS assays. These assays and screening approaches should accelerate drug discovery for neurodegenerative disorders and guide the development of screening approaches for other complex disease phenotypes.     

Identification of SARS-CoV-2 Papain-like Protease (PLpro) Inhibitors Using Combined Computational Approach**

In the current pandemic, finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.