Targeting programmed cell death 4 (PDCD4) with biogenic compounds in ARDS by Gaussian process‐based QSAR virtual screening

The programmed cell death 4 (PDCD4) has recently been recognized as a new and attractive target of acute respiratory distress syndrome. Here, we attempted to discover new and potent PDCD4 mediator ligands from biogenic compounds using a synthetic strategy of statistical virtual screening and experimental affinity assay. In the procedure, a Gaussian process‐based quantitative structure‐activity relationship regression predictor was developed and validated statistically based on a curated panel of structure‐based protein‐ligand affinity data. The predictor was integrated with pharmacokinetics analysis, chemical redundancy reduction, and flexible molecular docking to perform high‐throughput virtual screening against a distinct library of chemically diverse, drug‐like biogenic compounds. Consequently, 6 hits with top scores were selected, and their binding affinities to the recumbent protein of human PDCD4 were identified, 3 out of which were determined to have high or moderate affinity with K_d at micromolar level. Structural analysis of protein‐ligand complexes revealed that hydrophobic interactions and van der Waals contacts are the primary chemical forces to stabilize the complex architecture of PDCD4 with these mediator ligands, while few hydrogen bonds, salt bridges, and/or π‐π stacking at the complex interfaces confer selectivity and specificity for the protein‐ligand recognition. It is suggested that the statistical Gaussian process‐based quantitative structure‐activity relationship screening strategy can be successfully applied to rational discovery of biologically active compounds. The newly identified molecular entities targeting PDCD4 are considered as promising lead scaffolds to develop novel chemical therapeutics for acute respiratory distress syndrome.     

A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures

To realize the full potential of combinatorial chemistry-based drug discovery, generic and efficient tools must be developed that apply the strengths of diversity-oriented chemical synthesis to the identification and optimization of lead compounds for disease-associated protein targets. We report an affinity selection-mass spectrometry (AS-MS) method for protein-ligand affinity ranking and the classification of ligands by binding site. The method incorporates the following steps: (1) an affinity selection stage, where protein-binding compounds are selected from pools of ligands in the presence of varying concentrations of a competitor ligand, (2) a first chromatography stage to separate unbound ligands from protein-ligand complexes, and (3) a second chromatography stage to dissociate the ligands from the complexes for identification and quantification by MS. The ability of the competitor ligand to displace a target-bound library member, as measured by MS, reveals the binding site classification and affinity ranking of the mixture components. The technique requires no radiolabel incorporation or direct biochemical assay, no modification or immobilization of the compounds or target protein, and all reaction components, including any buffers or cofactors required for protein stability, are free in solution. We demonstrate the method for several compounds of wide structural variety against representatives of the most important protein classes in contemporary drug discovery, including novel ATP-competitive and allosteric inhibitors of the Akt-1 (PKB) and Zap-70 kinases, and previously undisclosed antagonists of the M(2) muscarinic acetylcholine receptor, a G-protein coupled receptor (GPCR). The theoretical basis of the technique is analyzed mathematically, allowing quantitative estimation of binding affinities and, in the case of allosteric interaction, absolute determination of binding cooperativity. The method is readily applicable to high-throughput screening hit triage, combinatorial library-based affinity optimization, and developing structure-activity relationships among multiple ligands to a given receptor.     

Multi-spectroscopic and HPLC Studies of the Interaction Between Estradiol and Cyclophosphamide With Human Serum Albumin: Binary and Ternary Systems

Drug resistance is a phenomenon that frequently impairs a proper treatment of infections and cancer with chemotherapy. Multidrug efflux transporters extrude structurally dissimilar organic compounds often providing resistance to multiple toxic chemotherapeutic agents. The quantitative analysis of drug efflux requires measuring the affinity of ligands. In this work, the interaction between cyclophosphamide (Cyc) and estradiol (ES) with human serum albumin (HSA) was studied by fluorescence polarization, circular dichroism and high-performance liquid chromatography (HPLC) under physiological conditions (pH = 7.4). Gradual addition of HSA led to a marked increase in fluorescence polarization. Our assays indicated that the protein was bound to these drugs with different K _d. Also, the Hill coefficient showed a simple drug binding process with no cooperativity. Circular dichroism results revealed the occurrence of conformational changes in HSA molecules by the binding of Cyc and ES. The protein binding of the drug was studied by HPLC. Our results indicated that the drug was bound to the protein and that the presence of a second drug affected the interaction and resistance between the first drug and the protein.     

Two-dimensional turbulent flow chromatography coupled on-line to liquid chromatography–mass spectrometry for solution-based ligand screening against multiple proteins

We present herein a novel bioseparation/chemical analysis strategy for protein–ligand screening and affinity ranking in compound mixtures, designed to increase screening rates and improve sensitivity and ruggedness in performance. The strategy is carried out by combining on-line two-dimensional turbulent flow chromatography (2D-TFC) with liquid chromatography–mass spectrometry (LC–MS), and accomplished through the following steps: (1) a reversed-phase TFC stage to separate the protein/ligand complex from the unbound free molecules, (2) an on-line dissociation process to release the bound ligands from the complexes, and (3) a second mixed-mode cation-exchange/reversed-phase TFC stage to trap the bound ligands and to remove the proteins and salts, followed by LC–MS analysis for identification and determination of the binding affinities. The technique can implement an ultra-fast isolation of protein/ligand complex with the retention time of a complex peak in about 5 s, and on-line prepare the “clean” sample to be directly compatible with the LC–MS analysis. The improvement in performance of this 2D-TFC/LC–MS approach over the conventional approach has been demonstrated by determining affinity-selected ligands of the target proteins acetylcholinesterase and butyrylcholinesterase from a small library with known binding affinities and a steroidal alkaloid library composed of structurally similar compounds. Our results show that 2D-TFC/LC–MS is a generic and efficient tool for high-throughput screening of ligands with low-to-high binding affinities, and structure-activity relationship evaluation.     

SAR by MS

RNAs have recently emerged as an exciting new target for small molecule therapeutics. Conventional HTS discovery strategies measuring disruption of RNAprotein interactions have proven unsuccessful. We describe a ligand-based drug discovery strategy that addresses the inherent difficulties RNA targets. The strategy is based on: 1) using a MS spectrometry (MS)-based assay to measure the affinity of compounds for a target; 2) performing competitive binding experiments and molecular modeling with the motifs to determine the binding site(s) of the ligands; 3) design and synthesis of derivatives of interesting binders to establish the linking sites; 4) identifying the appropriate linker group using MS; 5) fusing motifs into a more complex structure to afford higher affinity compounds. Example of applying this strategy to identify new classes of lead molecules with affinity and specificity for ribosomal RNA targets will be presented.     

Fragment screening of cyclin G-associated kinase by weak affinity chromatography

Fragment-based drug discovery (FBDD) has become a new strategy for drug discovery where lead compounds are evolved from small molecules. These fragments form low affinity interactions (dissociation constant (K (D))?=?mM?-?μM) with protein targets, which require fragment screening methods of sufficient sensitivity. Weak affinity chromatography (WAC) is a promising new technology for fragment screening based on selective retention of fragments by a drug target. Kinases are a major pharmaceutical target, and FBDD has been successfully applied to several of these targets. In this work, we have demonstrated the potential to use WAC in combination with mass spectrometry (MS) detection for fragment screening of a kinase target-cyclin G-associated kinase (GAK). One hundred seventy fragments were selected for WAC screening by virtual screening of a commercial fragment library against the ATP-binding site of five different proteins. GAK protein was immobilized on a capillary HPLC column, and compound binding was characterized by frontal affinity chromatography. Compounds were screened in sets of 13 or 14, in combination with MS detection for enhanced throughput. Seventy-eight fragments (46?%) with K (D)?相似文献   

Drug screening of pharmaceutical discovery compounds by micro-size exclusion chromatography/mass spectrometry.

Micro-size exclusion chromatography coupled with capillary liquid chromatography (capLC) and mass spectrometry (MS) provides a rapid and simple approach to the preliminary screening of active ligands toward a specific target macromolecule. In this study, the effectiveness of this technique is demonstrated by a number of small molecule ligands with known binding affinities towards the protein target. All ligands were incubated together with a target protein under native conditions. Separation was then achieved by microcentrifugation where the high molecular weight (MW) compounds were selectively passed through the size-exclusion material. The retained low MW compounds were then recovered and analyzed by capLC/MS. The absence of the ligand indicated strong affinity towards the target, while ligand detection indicated inactivity. This assay demonstrated the drugs that were acting as strong inhibitors of Co-PDF from those showing to be comparatively inactive. The relative binding rank order of the drugs towards Co-PDF was also determined. The results were validated by a corresponding set of control experiments in which the target molecules were excluded from the process. In principle, high-throughput micro-size exclusion chromatography, coupled with capLC/MS, offers a powerful technique as a preliminary screen in determining both the strong binding affinity and the relative affinity rank ordering of ligands towards a specific target macromolecule, and is complementary with other analytical drug screening techniques.     

Micro-Scale Frontal Affinity Chromatography with Mass Spectrometric Detection: A New Method for the Screening of Compound Libraries

In one run the binding constants K_d for all the active components of a ligand library at sub-microgram quantities can be determined. A mixture of ligands is continuously infused through a column of immobilized receptor, and the eluent analyzed by electrospray mass spectrometry. From the affinity chromatogram produced (see picture) the breakthrough volume of a single compound and hence its K_d value can be determined.     

HAMS: High-Affinity Mass Spectrometry Screening. A High-Throughput Screening Method for Identifying the Tightest-Binding Lead Compounds for Target Proteins with No False Positive Identifications

A major challenge in drug discovery is the identification of high affinity lead compounds that bind a particular target protein; these leads are typically identified by high throughput screens. Mass spectrometry has become a detection method of choice in drug screening assays because the target and the ligand need not be modified. Label-free assays are advantageous because they can be developed more rapidly than assays requiring labels, and they eliminate the risk of the label interfering with the binding event. However, in commonly used MS-based screening methods, detection of false positives is a major challenge. Here, we describe a detection strategy designed to eliminate false positives. In this approach, the protein and the ligands are incubated together, and the non-binders are separated for detection. Hits (protein binders) are not detectable by MS after incubation with the protein, but readily identifiable by MS when the target protein is not present in the incubation media. The assay was demonstrated using three different proteins and hundreds of non-inhibitors; no false positive hits were identified in any experiment. The assay can be tuned to select for ligands of a particular binding affinity by varying the quantity of protein used and the immobilization method. As examples, the method selectively detected inhibitors that have K_i values of 0.2 μM, 50 pM, and 700 pM. These findings demonstrate that the approach described here compares favorably with traditional MS-based screening methods.

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A Universal DNA Aptamer that Recognizes Spike Proteins of Diverse SARS-CoV-2 Variants of Concern

We report on a unique DNA aptamer, denoted MSA52, that displays universally high affinity for the spike proteins of wildtype SARS-CoV-2 as well as the Alpha, Beta, Gamma, Epsilon, Kappa, Delta and Omicron variants. Using an aptamer pool produced from round 13 of selection against the S1 domain of the wildtype spike protein, we carried out one-round SELEX experiments using five different trimeric spike proteins from variants, followed by high-throughput sequencing and sequence alignment analysis of aptamers that formed complexes with all proteins. A previously unidentified aptamer, MSA52, showed K_d values ranging from 2 to 10 nM for all variant spike proteins, and also bound similarly to variants not present in the reselection experiments. This aptamer also recognized pseudotyped lentiviruses (PL) expressing eight different spike proteins of SARS-CoV-2 with K_d values between 20 and 50 pM, and was integrated into a simple colorimetric assay for detection of multiple PL variants. This discovery provides evidence that aptamers can be generated with high affinity to multiple variants of a single protein, including emerging variants, making it well-suited for molecular recognition of rapidly evolving targets such as those found in SARS-CoV-2.     

Ligand design by targeting a binding site water

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood. We investigated how altered interactions with a water network can influence ligand binding to a receptor. A series of ligands of the A_2A adenosine receptor, which either interacted with or displaced an ordered binding site water, were studied experimentally and by molecular dynamics simulations. An analog of the endogenous ligand that was unable to hydrogen bond to the ordered water lost affinity and this activity cliff was captured by molecular dynamics simulations. Two compounds designed to displace the ordered water from the binding site were then synthesized and evaluated experimentally, leading to the discovery of an A_2A agonist with nanomolar activity. Calculation of the thermodynamic profiles resulting from introducing substituents that interacted with or displaced the ordered water showed that the gain of binding affinity was enthalpy driven. Detailed analysis of the energetics and binding site hydration networks revealed that the enthalpy change was governed by contributions that are commonly neglected in structure-based drug optimization. In particular, simulations suggested that displacement of water from a binding site to the bulk solvent can lead to large energy contributions. Our findings provide insights into the molecular driving forces of protein–ligand binding and strategies for rational drug design.

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood.     

Protein-Templated Fragment Ligations—From Molecular Recognition to Drug Discovery

Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules (“fragments”) utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed.     

DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0.

DeepFrag is a machine-learning model designed to assist with lead optimization. It recommends appropriate fragment additions given the 3D structures of a protein receptor and bound small-molecule ligand.     

Exploring Weak Ligand–Protein Interactions by Long‐Lived NMR States: Improved Contrast in Fragment‐Based Drug Screening

Ligands that have an affinity for protein targets can be screened very effectively by exploiting favorable properties of long‐lived states (LLS) in NMR spectroscopy. In this work, we describe the use of LLS for competitive binding experiments to measure accurate dissociation constants of fragments that bind weakly to the ATP binding site of the N‐terminal ATPase domain of heat shock protein 90 (Hsp90), a therapeutic target for cancer treatment. The LLS approach allows one to characterize ligands with an exceptionally wide range of affinities, since it can be used for ligand concentrations [L] that are several orders of magnitude smaller than the dissociation constants K_D. This property makes the LLS method particularly attractive for the initial steps of fragment‐based drug screening, where small molecular fragments that bind weakly to a target protein must be identified, which is a difficult task for many other biophysical methods.     

Unpredictable stereochemical preferences for mu opioid receptor activity in an exhaustively stereodiversified library of 1,4-enediols

Using olefin cross-metathesis, we synthesized a novel stereodiversified library of compounds 3 containing a trans-1,4-enediol. Screening this library for mu opioid receptor (MOR) affinity identified multiple high-affinity ligands and revealed that the stereochemical configuration varied widely among those ligands having the highest affinity. It was not possible to predict the configurations of the most active compounds 3 on the basis of the configuration of endomorphin-2, a known MOR peptide ligand, validating the diversity-based approach to ligand discovery.     

Development of immobilized membrane-based affinity columns for use in the online characterization of membrane bound proteins and for targeted affinity isolations

Membranes obtained from cell lines that express or do not express a target membrane bound protein have been immobilized on a silica-based liquid chromatographic support or on the surface of an activated glass capillary. The resulting chromatographic columns have been placed in liquid chromatographic systems and used to characterize the target proteins and to identify small molecules that bind to the target. Membranes containing ligand gated ion channels, G-protein coupled receptors and drug transporters have been prepared and characterized. If a marker ligand has been identified for the target protein, frontal or zonal displacement chromatographic techniques can be used to determine binding affinities (K_d values) and non-linear chromatography can be used to assess the association (k_on) and dissociation (k_off) rate constants and the thermodynamics of the binding process. Membrane-based affinity columns have been created using membranes from a cell line that does not express the target protein (control) and the same cell line that expresses the target protein (experimental) after genomic transfection. The resulting columns can be placed in a parallel chromatography system and the differential retention between the control and experimental columns can be used to identify small molecules and protein that bind to the target protein. These applications will be illustrated using columns created using cellular membranes containing nicotinic acetylcholine receptors and the drug transporter P-glycoprotein.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

A high throughput metabolic stability screening workflow with automated assessment of data quality in pharmaceutical industry

One of the most commonly performed in vitro ADME assays during the lead generation and lead optimization stage of drug discovery is metabolic stability evaluation. Metabolic stability is typically assessed in liver microsomes, which contain Phase I metabolizing enzymes, mainly cytochrome P450 enzymes (CYPs). The amount of parent drug metabolized by these CYPs is determined by LC/MS/MS. The metabolic stability data are typically used to rank order compounds for in vivo evaluation. We describe a streamlined and intelligent workflow for the metabolic stability assay that permits high throughput analyses to be carried out while maintaining the standard of high quality. This is accomplished in the following ways: a novel post-incubation pooling strategy based on c Log D_3.0 values, coupled with ultra-performance liquid chromatography/tandem mass spectrometry (UPLC/MS/MS), enables sample analysis times to be reduced significantly while ensuring adequate chromatographic separation of compounds within a group, so as to reduce the likelihood of compound interference. Assay quality and fast turnaround of data reports is ensured by performing automated real-time intelligent re-analysis of discrete samples for compounds that do not pass user-definable criteria during the pooling analysis. Intelligent, user-independent data acquisition and data evaluation are accomplished via a custom visual basic program that ties together every step in the workflow, including cassette compound selection, compound incubation, compound optimization, sample analysis and re-analysis (when appropriate), data processing, data quality evaluation, and database upload. The workflow greatly reduces labor and improves data turnaround time while maintaining high data quality.     

Fluorescence detection of cytosine/guanine transversion based on a hydrogen bond forming ligand

In combination with abasic site (AP site)-containing oligodeoxynucleotides (ODNs), we demonstrate potential use of a hydrogen bond forming ligand, 2-amino-7-methyl-1,8-naphthyridine (AMND), for the fluorescence detection of the cytosine (C)/guanine (G) mutation sequence of the cancer repression gene p53. Our method is based on construction of the AP site in ODN duplexes, which allows small synthetic ligands to bind to target nucleobases accompanied by fluorescence signaling: an AP site-containing ODN is hybridized with a target ODN so as to place the AP site toward a target nucleobase, by which hydrophobic microenvironments are provided for ligands to recognize target nucleobases through hydrogen-bonding. In 10 mM sodium cacodylate buffer solutions (pH, 7.0) containing 100 mM NaCl and 1.0 mM EDTA, AMND is found to strongly bind to C (K_d=1.5×10⁻⁶ M) in the target ODN while the binding affinity for G is relatively moderate (K_d=50×10⁻⁶ M). Significant fluorescence quenching of AMND is observed only when binding to C, making it possible to judge the C/G transversion with the naked eye.