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.     

NMR-based analysis of protein–ligand interactions

Physiological processes are mainly controlled by intermolecular recognition mechanisms involving protein–protein and protein–ligand (low molecular weight molecules) interactions. One of the most important tools for probing these interactions is high-field solution nuclear magnetic resonance (NMR) through protein-observed and ligand-observed experiments, where the protein receptor or the organic compounds are selectively detected. NMR binding experiments rely on comparison of NMR parameters of the free and bound states of the molecules. Ligand-observed methods are not limited by the protein molecular size and therefore have great applicability for analysing protein–ligand interactions. The use of these NMR techniques has considerably expanded in recent years, both in chemical biology and in drug discovery. We review here three major ligand-observed NMR methods that depend on the nuclear Overhauser effect—transferred nuclear Overhauser effect spectroscopy, saturation transfer difference spectroscopy and water–ligand interactions observed via gradient spectroscopy experiments—with the aim of reporting recent developments and applications for the characterization of protein–ligand complexes, including affinity measurements and structural determination.     

High content screening for G protein-coupled receptors using cell-based protein translocation assays

G protein-coupled receptors (GPCRs) have been one of the most productive classes of drug targets for several decades, and new technologies for GPCR-based discovery promise to keep this field active for years to come. While molecular screens for GPCR receptor agonist- and antagonist-based drugs will continue to be valuable discovery tools, the most exciting developments in the field involve cell-based assays for GPCR function. Some cell-based discovery strategies, such as the use of beta-arrestin as a surrogate marker for GPCR function, have already been reduced to practice, and have been used as valuable discovery tools for several years. The application of high content cell-based screening to GPCR discovery has opened up additional possibilities, such as direct tracking of GPCRs, G proteins and other signaling pathway components using intracellular translocation assays. These assays provide the capability to probe GPCR function at the cellular level with better resolution than has previously been possible, and offer practical strategies for more definitive selectivity evaluation and counter-screening in the early stages of drug discovery. The potential of cell-based translocation assays for GPCR discovery is described, and proof-of-concept data from a pilot screen with a CXCR4 assay are presented. This chemokine receptor is a highly relevant drug target which plays an important role in the pathogenesis of inflammatory disease and also has been shown to be a co-receptor for entry of HIV into cells as well as to play a role in metastasis of certain cancer cells.     

The SHAPES strategy: an NMR-based approach for lead generation in drug discovery.

BACKGROUND: Recently, it has been shown that nuclear magnetic resonance (NMR) may be used to identify ligands that bind to low molecular weight protein drug targets. Recognizing the utility of NMR as a very sensitive method for detecting binding, we have focused on developing alternative approaches that are applicable to larger molecular weight drug targets and do not require isotopic labeling. RESULTS: A new method for lead generation (SHAPES) is described that uses NMR to detect binding of a limited but diverse library of small molecules to a potential drug target. The compound scaffolds are derived from shapes most commonly found in known therapeutic agents. NMR detection of low (microM-mM) affinity binding is achieved using either differential line broadening or transferred NOE (nuclear Overhauser effect) NMR techniques. CONCLUSIONS: The SHAPES method for lead generation by NMR is useful for identifying potential lead classes of drugs early in a drug design program, and is easily integrated with other discovery tools such as virtual screening, high-throughput screening and combinatorial chemistry.     

Design and characterization of a functional library for NMR screening against novel protein targets

In the past few years, NMR has been extensively utilized as a screening tool for drug discovery using various types of compound libraries. The designs of NMR specific chemical libraries that utilize a fragment-based approach based on drug-like characteristics have been previously reported. In this article, a new type of compound library will be described that focuses on aiding in the functional annotation of novel proteins that have been identified from various ongoing genomics efforts. The NMR functional chemical library is comprised of small molecules with known biological activity such as: co-factors, inhibitors, metabolites and substrates. This functional library was developed through an extensive manual effort of mining several databases based on known ligand interactions with protein systems. In order to increase the efficiency of screening the NMR functional library, the compounds are screened as mixtures of 3-4 compounds that avoids the need to deconvolute positive hits by maintaining a unique NMR resonance and function for each compound in the mixture. The functional library has been used in the identification of general biological function of hypothetical proteins identified from the Protein Structure Initiative.     

Solution nuclear magnetic resonance spectroscopy techniques for probing intermolecular interactions

Nuclear magnetic resonance (NMR) spectroscopy in solution has evolved into a powerful technique for structure determination of proteins and nucleic acids. More recently, a number of NMR-based approaches have been developed to monitor and characterize intermolecular interactions. These approaches offer unique advantages over other techniques and find their utility in both structural biology and drug discovery. We will report on basic principles and recent examples of the application of such NMR methodologies to characterize protein-protein interactions and for ligand binding studies and drug discovery.     

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.     

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.     

Cellular automata and its applications in protein bioinformatics

With the explosion of protein sequences generated in the postgenomic era, it is highly desirable to develop high-throughput tools for rapidly and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. The knowledge thus obtained can help us timely utilize these newly found protein sequences for both basic research and drug discovery. Many bioinformatics tools have been developed by means of machine learning methods. This review is focused on the applications of a new kind of science (cellular automata) in protein bioinformatics. A cellular automaton (CA) is an open, flexible and discrete dynamic model that holds enormous potentials in modeling complex systems, in spite of the simplicity of the model itself. Researchers, scientists and practitioners from different fields have utilized cellular automata for visualizing protein sequences, investigating their evolution processes, and predicting their various attributes. Owing to its impressive power, intuitiveness and relative simplicity, the CA approach has great potential for use as a tool for bioinformatics.     

Modeling & Informatics at Vertex Pharmaceuticals Incorporated: our philosophy for sustained impact

Molecular modelers and informaticians have the unique opportunity to integrate cross-functional data using a myriad of tools, methods and visuals to generate information. Using their drug discovery expertise, information is transformed to knowledge that impacts drug discovery. These insights are often times formulated locally and then applied more broadly, which influence the discovery of new medicines. This is particularly true in an organization where the members are exposed to projects throughout an organization, such as in the case of the global Modeling & Informatics group at Vertex Pharmaceuticals. From its inception, Vertex has been a leader in the development and use of computational methods for drug discovery. In this paper, we describe the Modeling & Informatics group at Vertex and the underlying philosophy, which has driven this team to sustain impact on the discovery of first-in-class transformative medicines.     

Phenotypic taxonomy and metabolite profiling in microbial drug discovery

Microorganisms and in particular actinomycetes and microfungi are known to produce a vast number of bioactive secondary metabolites. For industrially important fungal genera such as Penicillium and Aspergillus the production of these compounds has been demonstrated to be very consistent at the species level. This means that direct metabolite profiling techniques such as direct injection mass spectrometry or NMR can easily be used for chemotyping/metabolomics of strains from both culture collections and natural samples using modern informatics tools. In this review we discuss chemotyping/metabolomics as part of intelligent screening and highlight how it can be used for identification and classification of filamentous fungi and for the discovery of novel compounds when used in combination with modern methods for dereplication. In our opinion such approaches will be important for future effective drug discovery strategies, especially for dereplication of culture collections in order to avoid redundancy in the selection of species. This will maximize the chemical diversity of the microbial natural product libraries that can be generated from fungal collections.     

Efficient Synthesis,Characterization, In Vitro Antibacterial and Antifungal Activity Study and Computational Tool for Prediction of Molecular Properties of Some Novel Schiff's Base via Betti's Protocol and Azetidinones

Series of new substituted pyrazolone derivatives via Betti's condensation reaction of pyrazolone and different aldehydes has been reported successfully under ambient reaction conditions. The structural elucidation of newly synthesized compounds was done using analytical and spectroscopic tools such as elemental analysis, ¹H NMR, ¹³C NMR, and mass spectroscopy. Physicochemical parameters, toxicity profiles and drug likeness properties were studied using various bioinformaticals tools like Osiris and molinspiration. These results viz., the good correlations between the inhibitory activities and the computational values make the molecules available for future protein–ligand interaction studies. It further provided useful information in understanding the structural and chemical features of the drug in designing and finding new potential inhibitors.     

Knowing when to give up: early-rejection stratagems in ligand docking

Virtual screening is an important resource in the drug discovery community, of which protein–ligand docking is a significant part. Much software has been developed for this purpose, largely by biochemists and those in related disciplines, who pursue ever more accurate representations of molecular interactions. The resulting tools, however, are very processor-intensive. This paper describes some initial results from a project to review computational chemistry techniques for docking from a non-chemistry standpoint. An abstract blueprint for protein–ligand docking using empirical scoring functions is suggested, and this is used to discuss potential improvements. By introducing computer science tactics such as lazy function evaluation, dramatic increases to throughput can and have been realized using a real-world docking program. Naturally, they can be extended to any system that approximately corresponds to the architecture outlined.     

TINS, target immobilized NMR screening: an efficient and sensitive method for ligand discovery

We propose a ligand screening method, called TINS (target immobilized NMR screening), which reduces the amount of target required for the fragment-based approach to drug discovery. Binding is detected by comparing 1D NMR spectra of compound mixtures in the presence of a target immobilized on a solid support to a control sample. The method has been validated by the detection of a variety of ligands for protein and nucleic acid targets (K(D) from 60 to 5000 muM). The ligand binding capacity of a protein was undiminished after 2000 different compounds had been applied, indicating the potential to apply the assay for screening typical fragment libraries. TINS can be used in competition mode, allowing rapid characterization of the ligand binding site. TINS may allow screening of targets that are difficult to produce or that are insoluble, such as membrane proteins.     

Applications of protein microarray technology

Protein microarrays, an emerging class of proteomic technologies, are quickly becoming essential tools for large-scale and high throughput biochemistry and molecular biology. Recent progress has been made in all the key steps of protein microarray fabrication and application, such as the large-scale cloning of expression-ready prokaryotic and eukaryotic ORFs, high throughput protein purification, surface chemistry, protein delivery systems, and detection methods. Two classes of protein microarrays are currently available: analytical and functional protein microarrays. In the case of analytical protein microarrays, well-characterized molecules with specific activity, such as antibodies, peptide-MHC complexes, or lectins, are used as immobilized probes. These arrays have become one of the most powerful multiplexed detection platforms. Functional protein microarrays are being increasingly applied to many areas of biological discovery, including drug target identification/validation and studies of protein interaction, biochemical activity, and immune responses. Great progress has been achieved in both classes of protein microarrays in terms of sensitivity and specificity, and new protein microarray technologies are continuing to emerge. Finally, protein microarrays have found novel applications in both scientific research and clinical diagnostics.     

Drug Design: Where We Are and Future Prospects

Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.     

Exploring Multi-Subsite Binding Pockets in Proteins: DEEP-STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

Ligand-based NMR techniques to study protein–ligand interactions are potent tools in drug design. Saturation transfer difference (STD) NMR spectroscopy stands out as one of the most versatile techniques, allowing screening of fragments libraries and providing structural information on binding modes. Recently, it has been shown that a multi-frequency STD NMR approach, differential epitope mapping (DEEP)-STD NMR, can provide additional information on the orientation of small ligands within the binding pocket. Here, the approach is extended to a so-called DEEP-STD NMR fingerprinting technique to explore the binding subsites of cholera toxin subunit B (CTB). To that aim, the synthesis of a set of new ligands is presented, which have been subject to a thorough study of their interactions with CTB by weak affinity chromatography (WAC) and NMR spectroscopy. Remarkably, the combination of DEEP-STD NMR fingerprinting and Hamiltonian replica exchange molecular dynamics has proved to be an excellent approach to explore the geometry, flexibility, and ligand occupancy of multi-subsite binding pockets. In the particular case of CTB, it allowed the existence of a hitherto unknown binding subsite adjacent to the GM1 binding pocket to be revealed, paving the way to the design of novel leads for inhibition of this relevant toxin.     

Unmodified methodologies in target discovery for small molecule drugs: A rising star

Target discovery, involving target identification and validation, is the prerequisite for drug discovery and screening. Novel methodologies and technologies for the precise discovery and confirmation of drug targets are powerful tools in understanding the disease, looking for a drug and elucidating the mechanism of drug treatment. Among the common target identification and confirmation methods, the modified method is time-consuming and laborious, which may reduce or change the activity of natural products. The unmodified methods developed in recent years without chemical modification have gradually become an important means of studying drug targets. A wide range of unmodified approaches have been reported, introducing and analyzing the recent emerging methodologies and technologies. This review highlights the advantages and limitations of these methods for the application of drug target discovery and presents an overview of their contributions to the target discovery of small molecule drugs. The application and future development trends of methodologies in target discovery are also prospected to provide a reference for drug target research.     

High-throughput, accurate mass liquid chromatography/tandem mass spectrometry on a quadrupole time-of-flight system as a 'first-line' approach for metabolite identification studies

Throughput for drug metabolite identification studies has been increased significantly by the combined use of accurate mass liquid chromatography/tandem mass spectrometry (LC/MS/MS) data on a quadrupole time-of-flight (QTOF) system and targeted data analysis procedures. Employed in concert, these tools have led to the implementation of a semi-automated high-throughput metabolite identification strategy that has been incorporated successfully into lead optimization efforts in drug discovery. The availability of elemental composition data on precursor and all fragment ions in each spectrum has greatly enhanced confidence in ion structure assignments, while computer-based algorithms for defining sites of biotransformation based upon mass shifts of diagnostic fragment ions have facilitated identification of positions of metabolic transformation in drug candidates. Adoption of this technology as the 'first-line' approach for in vitro metabolite profiling has resulted in the analysis of as many as 21 new chemical entities on one day from diverse structural classes and therapeutic programs.     

Computer-aided drug design platform using PyMOL

The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.