Multiple ligand detection and affinity measurement by ultrafiltration and mass spectrometry analysis applied to fragment mixture screening

Binding affinity of a small molecule drug candidate to a therapeutically relevant biomolecular target is regarded the first determinant of the candidate's efficacy. Although the ultrafiltration-LC/MS (UF-LC/MS) assay enables efficient ligand discovery for a specific target from a mixed pool of compounds, most previous analysis allowed for relative affinity ranking of different ligands. Moreover, the reliability of affinity measurement for multiple ligands with UF-LC/MS has hardly been strictly evaluated. In this study, we examined the accuracy of K_d determination through UF-LC/MS by comparison with classical ITC measurement. A single-point K_d calculation method was found to be suitable for affinity measurement of multiple ligands bound to the same target when binding competition is minimized. A second workflow based on analysis of the unbound fraction of compounds was then developed, which simplified sample preparation as well as warranted reliable ligand discovery. The new workflow implemented in a fragment mixture screen afforded rapid and sensitive detection of low-affinity ligands selectively bound to the RNA polymerase NS5B of hepatitis C virus. More importantly, ligand identification and affinity measurement for mixture-based fragment screens by UF-LC/MS were in good accordance with single ligand evaluation by conventional SPR analysis. This new approach is expected to become a valuable addition to the arsenal of high-throughput screening techniques for fragment-based drug discovery.     

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   

Discovering protein-binding RNA motifs with a generative model of RNA sequences

Recent advances in high-throughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing protein-binding RNA sequences and motifs using a long short-term memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the protein-binding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated protein-binding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.     

Aminoglycoside microarrays to explore interactions of antibiotics with RNAs and proteins

RNA is an important target for drug discovery efforts. Several clinically used aminoglycoside antibiotics bind to bacterial rRNA and inhibit protein synthesis. Aminoglycosides, however, are losing efficacy due to their inherent toxicity and the increase in antibiotic resistance. Targeting of other RNAs is also becoming more attractive thanks to the discovery of new potential RNA drug targets through genome sequencing and biochemical efforts. Identification of new compounds that target RNA is therefore urgent, and we report here on the development of rapid screening methods to probe binding of low molecular weight ligands to proteins and RNAs. A series of aminoglycosides has been immobilized onto glass microscope slides, and binding to proteins and RNAs has been detected by fluorescence. Construction and analysis of the arrays is completed by standard DNA genechip technology. Binding of immobilized aminoglycosides to proteins that are models for study of aminoglycoside toxicity (DNA polymerase and phospholipase C), small RNA oligonucleotide mimics of aminoglycoside binding sites in the ribosome (rRNA A-site mimics), and a large (approximately 400 nucleotide) group I ribozyme RNA is detected. The ability to screen large RNAs alleviates many complications associated with binding experiments that use isolated truncated regions from larger RNAs. These studies lay the foundation for rapid identification of small organic ligands from combinatorial libraries that exhibit strong and selective RNA binding while displaying decreased affinity to toxicity-causing proteins.     

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.     

Peptide quinoline conjugates: a new class of RNA-binding molecules

[reaction: see text] A synthesis of 4,8-disubstituted 2-phenylquinoline amino acids is reported with the incorporation of one example into a peptide by solid-phase synthesis. The phenylquinoline-containing peptide binds an RNA target with nanomolar affinity (K(D) = 208 nM). The strategy can be used to prepare a variety of 2-substituted quinoline amino acids for alteration of affinity in intercalator peptides. Since quinolones represent an important class of antibacterials, these compounds may be useful in the discovery of new antibacterial agents.     

Identification of a nanomolar affinity α-synuclein fibril imaging probe by ultra-high throughput in silico screening

Small molecules that bind with high affinity and specificity to fibrils of the α-synuclein (αS) protein have the potential to serve as positron emission tomography (PET) imaging probes to aid in the diagnosis of Parkinson''s disease and related synucleinopathies. To identify such molecules, we employed an ultra-high throughput in silico screening strategy using idealized pseudo-ligands termed exemplars to identify compounds for experimental binding studies. For the top hit from this screen, we used photo-crosslinking to confirm its binding site and studied the structure–activity relationship of its analogs to develop multiple molecules with nanomolar affinity for αS fibrils and moderate specificity for αS over Aβ fibrils. Lastly, we demonstrated the potential of the lead analog as an imaging probe by measuring binding to αS-enriched homogenates from mouse brain tissue using a radiolabeled analog of the identified molecule. This study demonstrates the validity of our powerful new approach to the discovery of PET probes for challenging molecular targets.     

Novel strategy for three-dimensional fragment-based lead discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.     

Defining the RNA internal loops preferred by benzimidazole derivatives via 2D combinatorial screening and computational analysis

RNA is an important therapeutic target; however, RNA targets are generally underexploited due to a lack of understanding of the small molecules that bind RNA and the RNA motifs that bind small molecules. Herein, we describe the identification of the RNA internal loops derived from a 4096 member 3 × 3 nucleotide loop library that are the most specific and highest affinity binders to a series of four designer, druglike benzimidazoles. These studies establish a potentially general protocol to define the highest affinity and most specific RNA motif targets for heterocyclic small molecules. Such information could be used to target functionally important RNAs in genomic sequence.     

In vitro evolution of ligands to the membrane protein caveolin

Membrane proteins comprise a third of the human genome, yet present challenging targets for reverse chemical genetics. For example, although implicated in numerous diseases including multiple myeloma, the membrane protein caveolin-1 appears to offer a poor target for the discovery of synthetic ligands due to its largely unknown structure and insolubility. To break this impasse and identify new classes of caveolae controlling lead compounds, we applied phage-based, reverse chemical genetics for the discovery of caveolin-1 ligands derived from the anti-HIV therapeutic T20. Substitution of homologous residues into the T20 sequence used a process analogous to medicinal chemistry for the affinity maturation to bind caveolin. The resultant caveolin-1 ligands bound with >1000-fold higher affinity than wild-type T20. Two types of ELISAs and isothermal titration calorimetry (ITC) measurements demonstrated high affinity binding to caveolin by the T20 variants with K(d) values in the 150 nM range. Microscopy experiments with the highest affinity caveolin ligands confirmed colocalization of the ligands with endogenous caveolin in NIH 3T3 cells. The results establish the foundation for targeting caveolin and caveolae formation in living cells.     

活性导向的化学探针在氨基酸反应活性表征中的应用进展

原创药物的研制得益于蛋白质新靶标的发现,而新靶标的发现依赖于高可信度、高通量的药物-蛋白质相互作用分析方法。蛋白质作为生命功能的执行者,其表达量、空间定位与结构差异直接影响药效的发挥。目前,超过85%的蛋白质尚被认为是无法成药的,主要原因是缺少药物分子靶向的空腔以及相应的反应活性位点。因此,基于蛋白质组学层次实现对氨基酸反应活性位点的表征成为原创共价靶向药物设计的关键,也是克服难以成药靶标蛋白问题的关键。近年来,质谱技术的飞速发展极大地推动了基于蛋白质组学技术的药物-靶蛋白相互作用研究。其中基于活性的蛋白质组分析(ABPP)策略是利用活性位点导向的化学探针分子在复杂样品中实现功能状态酶和药物靶标等蛋白质的检测。基于化学探针的开发和质谱定量技术的发展,ABPP技术在氨基酸反应活性表征研究中展现出重要的应用潜力,将助力于药物新靶标的发现和药物先导化合物的开发。ABPP策略主要基于蛋白质的活性特征进行富集,活性探针作为ABPP策略的核心,近年来取得了飞速进展。该文回顾了ABPP策略的发展历程,重点介绍基于广谱活性探针的ABPP技术在多种氨基酸反应活性筛选领域的研究进展,并对其在药物靶点发现中...     

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.     

Exploratory Study on the RNA‐Binding Structural Motifs by Library Screening Targeting pre‐miRNA‐29 a

The metabolic stream of microRNA (miRNA) production, the so‐called maturation process of miRNAs, became one of important metabolic paths for drug‐targeting to modulate the expression of genes related to a number of diseases. We carried out discovery studies on small molecules binding to the precursor of miR‐29a (pre‐miR‐29a) from a chemical library containing 41 119 compounds (AQ library) by the fluorescent indicator displacement (FID) assay using the xanthone derivative X2SdiMe as a fluorescent indicator. The FID assay provided 1075 compounds, which showed an increase of fluorescence. These compounds were subsequently submitted to a binding analysis in a surface plasmon resonance (SPR) assay on a pre‐miR‐29a immobilized surface. 21 hit compounds were identified with a good reproducibility in the binding. These compounds have not been reported to bind to RNA until now and can be classified into two groups on the basis of the kinetics in the binding. To gain more information on the motif structures that could be necessary for the binding to pre‐miR‐29a, 19 substructures were selected from the hit compounds. The substructure library (SS library) which consisted of 362 compounds was prepared from the AQ library. An SPR assay of the SS library on pre‐miR‐29a‐immobilized surface suggested that five substructures could potentially be important structural motifs to bind to pre‐miR‐29a. These studies demonstrate that the combination of FID‐based screening of chemical library and subsequent SPR assay would be one way for obtaining practical solutions for the discovery of molecules which bind to the target pre‐miRNAs.     

A generic fast solid-phase extraction high-performance liquid chromatography/mass spectrometry method for high-throughput drug discovery

High-performance liquid chromatography/mass spectrometry (HPLC/MS) is increasingly perceived to be an essential tool in drug discovery at many key steps, like drug screening, lead identification, ADME profiling, and drug metabolism and pharmacology studies. High-throughput screenings in the early phase for metabolic stability, protein binding, permeability (ADME) and bioavailability are widely used to weed out compounds that do not exhibit the necessary characteristics. For such high-throughput LC/MS studies, a generic LC/MS method that can be used for a variety of compounds is desired. In this study, we used a small set of compounds with a wide range of properties to guide method development, and achieved a sample throughput of 1.7 min/sample. Here, we present a generic fast method that achieves good peak separation and peak shape on conventional HPLC systems using a column-switching mechanism for on-line solid-phase extraction (SPE)-HPLC/MS analysis. The method has a linear response range from 1 to 500 nM for the tested compounds. When a larger set of 658 randomly picked small molecules were analyzed using this method, 612 were observed with good signal intensity and HPLC peak shapes. This generic fast SPE-LC/MS method has been used to screen more than 1.5 million compounds repetitively against over 200 protein targets for hit confirmation and semi-quantitation of binding constants from biological assays. Over 7000 different compounds for a variety of protein-binding assays have been studied using this method for quantitative analysis as well.     

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications

ABSTRACT: BACKGROUND: Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. RESULTS: We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system. As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. CONCLUSIONS: This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.     

Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study

Polyphenol oxidases (PPOs)/tyrosinases are metal-dependent enzymes and known as important targets for melanogenesis. Although considerable attempts have been conducted to control the melanin-associated diseases by using various inhibitors. However, the exploration of the best anti-melanin inhibitor without side effect still remains a challenge in drug discovery. In present study, protein structure prediction, ligand-based pharmacophore modeling, virtual screening, molecular docking and dynamic simulation study were used to screen the strong novel inhibitor to cure melanogenesis. The 3D structures of PPO1 and PPO2 were built through homology modeling, while the 3D crystal structures of PPO3 and PPO4 were retrieved from PDB. Pharmacophore modeling was performed using LigandScout 3.1 software and top five models were selected to screen the libraries (2601 of Aurora and 727, 842 of ZINC). Top 10 hit compounds (C1-10) were short-listed having strong binding affinities for PPO1-4. Drug and synthetic accessibility (SA) scores along with absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment were employed to scrutinize the best lead hit. C4 (name) hit showed the best predicted SA score (5.75), ADMET properties and drug-likeness behavior among the short-listed compounds. Furthermore, docking simulations were performed to check the binding affinity of C1-C10 compounds against target proteins (PPOs). The binding affinity values of complex between C4 and PPOs were higher than those of other complexes (−11.70, −12.1, −9.90 and −11.20 kcal/mol with PPO1, PPO2, PPO3, or PPO4, respectively). From comparative docking energy and binding analyses, PPO2 may be considered as better target for melanogenesis than others. The potential binding modes of C4, C8 and C10 against PPO2 were explored using molecular dynamics simulations. The root mean square deviation and fluctuation (RMSD/RMSF) graphs results depict the significance of C4 over the other compounds. Overall, bioactivity and ligand efficiency profiles suggested that the proposed hit may be more effective inhibitors for melanogenesis.     

Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.     

Targeting structural features of viral genomes with a nano-sized supramolecular drug

RNA targeting is an exciting frontier for drug design. Intriguing targets include functional RNA structures in structurally-conserved untranslated regions (UTRs) of many lethal viruses. However, computational docking screens, valuable in protein structure targeting, fail for inherently flexible RNA. Herein we harness MD simulations with Markov state modeling to enable nanosize metallo-supramolecular cylinders to explore the dynamic RNA conformational landscape of HIV-1 TAR untranslated region RNA (representative for many viruses) replicating experimental observations. These cylinders are exciting as they have unprecedented nucleic acid binding and are the first supramolecular helicates shown to have anti-viral activity in cellulo: the approach developed in this study provides additional new insight about how such viral UTR structures might be targeted with the cylinder binding into the heart of an RNA-bulge cavity, how that reduces the conformational flexibility of the RNA and molecular details of the insertion mechanism. The approach and understanding developed represents a new roadmap for design of supramolecular drugs to target RNA structural motifs across biology and nucleic acid nanoscience.

MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion.     

Structure-switching signaling aptamers: transducing molecular recognition into fluorescence signaling

The development of aptamer technology considerably broadens the utility of nucleic acids as molecular recognition elements, because it allows the creation of DNA or RNA molecules for binding a wide variety of analytes (targets) with high affinity and specificity. Several recent studies have focused on developing rational design strategies for transducing aptamer-target recognition events into easily detectable signals, so that aptamers can be widely exploited for detection directed applications. We have devised a generalizable strategy for designing nonfluorescent aptamers that can be turned into fluorescence-signaling reporters. The resultant signaling probes are denoted "structure-switching signaling aptamers" as they report target binding by switching structures from DNA/DNA duplex to DNA/target complex. The duplex is formed between a fluorophore-labeled DNA aptamer and an antisense DNA oligonucleotide modified with a quencher (denoted QDNA). In the absence of the target, the aptamer hybridizes with QDNA, bringing the fluorophore into close proximity of the quencher for efficient fluorescence quenching. When this system is exposed to the target, the aptamer switches its binding partner from QDNA to the target. This structure-switching event is coupled to the generation of a fluorescent signal through the departure of QDNA, permitting the real-time monitoring of the aptamer-target recognition. In this article, we discuss the conceptual framework of the structure-switching approach, the essential features of structure-switching signaling aptamers as well as remaining challenges and possible solutions associated with this new methodology.