Ligand-target Interactions Research Articles

Quinone-based molecular electrochemistry and their contributions to medicinal chemistry: A look at the present and future

Abstract Molecular electrochemistry is closely linked to life sciences. Electron transfers play important roles in the bioactivation of redox-active drugs, in their metabolism/catabolism, and in their targeted release at precise destinations and frequently promote their ligand–target interactions. Altogether, this rich chemistry and the complexity of cellular environments and biocompartmentation often impede full investigation in situ of the whole chain of processes that sustain their therapeutic applications. Conversely, electrochemical ex situ investigations of drug properties and interactions performed in aqueous/aprotic/micellar/membrane/cell-mimetic media, combined with in vitro and in vivo data, are expected to provide extremely useful information on these processes. Therein, considering the ubiquitous case of quinones, we exemplified how such strategies allow controlling their beneficial or negative impact on cellular environments.

In Silico Investigation of the SARS CoV2 Protease with Thymoquinone, the Major Constituent of Nigella Sativa.

The COVID-19 caused by a new type of coronavirus has emerged from China and led to thousands of deaths globally. Despite many groups engaged in studying the newly emerged virus and searching for the treatment, the understanding of the SARS-CoV2 target ligand interactions represents a key challenge. Several studies are being conducted to identify potential treatment. Alternatively, the results of numerous studies have shown that protease inhibitors can be a genuine leader in research. The antiviral activity and beneficial effect against respiratory disorders of thymoquinone have been largely demonstrated. The aim of this study is to evaluate in silico the inhibition of the replication of SARS CoV2 by thymoquinone. This is a molecular simulation study using SARS CoV2 protease and thymoquinone structures provided by Protein Data Bank. The preliminary results have shown that thymoquinone may have inhibitory activities against SARS CoV2 protease. Furthermore, given the demonstrated results of thymoquinone, we can conclude that it may be considered as an effective or adjuvant treatment for SARS CoV2 infection.

NMR spectroscopy: the swiss army knife of drug discovery.

Nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful tool within drug discovery over the last two decades. While traditionally being used by medicinal chemists for small molecule structure elucidation, it can also be a valuable tool for the identification of small molecules that bind to drug targets, for the characterization of target-ligand interactions and for hit-to-lead optimization. Here, we describe how NMR spectroscopy is integrated into the Pfizer drug discovery pipeline and how we utilize this approach to identify and validate initial hits and generate leads.

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery.

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand interactions with diverse binding affinities. NMR spectroscopy is able to identify fragments that bind weakly to a target, making it valuable for identifying hits targeting undruggable sites. In this review, we summarize the roles of solution NMR spectroscopy in drug discovery. We describe some methods that are used in identifying fragments, understanding the mechanism of action for a ligand, and monitoring the conformational changes of a target induced by ligand binding. A number of studies have proven that 19F-NMR is very powerful in screening fragments and detecting protein conformational changes. In-cell NMR will also play important roles in drug discovery by elucidating protein-ligand interactions in living cells.

In silico structure prediction, molecular docking and dynamic simulation studies on G Protein-Coupled Receptor 116: a novel insight into breast cancer therapy

G Protein-Coupled Receptor gains more importance in cancer research; because of their key role in several physiologic functions of cells. However, most of the GPCR’s are orphan receptors, this hampers the finding of drugs against GPCR. G Protein-Coupled Receptor 116 is an adhesion orphan receptor that intensifies the invasion of cells in Triple-Negative Breast Cancer. In this study, existing FDA approved anticancer drugs were chosen as ligands and molecular docking was performed using in silico protein model of GPR116. Molecular interaction was analyzed carefully to identify the crucial amino acids present in binding pocket. Molecular dynamics simulations study executed to verify the structural and dynamic properties of Doxorubicin–GPR116 protein complex. The results have shown that Doxorubicin, Neratinib maleate, Epirubicin, and Lapatinib Ditosylate have good interaction with GPR116 binding site. Tyrosine 195 (Y195), Cysteine 196 (C196), Argenine 197 (R197), and Tryptophan 100 (W100) are commonly found in the majority of ligand–target interaction, hence based on the computational studies selective amino acids might be crucial for functional properties. Further to confirm crucial amino acids, computational mutation studies were executed. Molecular docking analysis with mutated GPR116 disclosed that significant variation in G score compared withligand–native protein interaction. Hence, the theoretical confirmatory structural properties changes support to prove selective crucial amino acids play the significant role in ligand binding. Molecular dynamic simulation results reveal that the interaction was stable throughout the MD simulation. To the best of our prognosis, GPR116 could be the best molecular target for breast cancer drug discovery. Communicated by Ramaswamy H. Sarma

Tackling the SARS-CoV-2 main protease using hybrid derivatives of 1,5-disubstituted tetrazole-1,2,3-triazoles: an in silico assay

In regard to the actual public health global emergency and, based on the state of the art about the ways to inhibit the SARS-CoV-2 treating the COVID19, a family of 1,5-disubstituted tetrazole-1,2,3-triazoles, previously synthesized, have been evaluated through in silico assays against the main protease of the mentioned virus (CoV-2-MPro). The results show that three of these compounds present a more favorable interaction with the selected target than the co-crystallized molecule, which is a peptide-like derivative. It was also found that also hydrophobic interactions play a key role in the ligand-target molecular couplings, due to the higher hydrophobic surfaces into the active site. Finally, a pharmacophore model has been proposed based on the results below, and a family of 1,5-DT derivatives has been designed and tested with the same methods employed in this work. It was concluded that the compound with the isatin as a substituent (P8) present the higher ligand-target interaction, which makes this a strong drug candidate against COVID19, due can inhibit the CoV-2-MProprotein.

Mapping the S1 and S1' subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents.

The cysteine protease cruzipain is considered to be a validated target for therapeutic intervention in the treatment of Chagas disease. A series of 26 new compounds were designed, synthesized, and tested against the recombinant cruzain (Cz) to map its S1/S1´ subsites. The same series was evaluated on a panel of four human cysteine proteases (CatB, CatK, CatL, CatS) and Leishmania mexicana CPB, which is a potential target for the treatment of cutaneous leishmaniasis. The synthesized compounds are dipeptidyl nitriles designed based on the most promising combinations of different moieties in P1 (ten), P2 (six), and P3 (four different building blocks). Eight compounds exhibited a Ki smaller than 20.0 nM for Cz, whereas three compounds met these criteria for LmCPB. Three inhibitors had an EC50 value of ca. 4.0 μM, thus being equipotent to benznidazole according to the antitrypanosomal effects. Our mapping approach and the respective structure-activity relationships provide insights into the specific ligand-target interactions for therapeutically relevant cysteine proteases.

Steroid sulfatase inhibiting lanostane triterpenes – Structure activity relationship and in silico insights

Steroid sulfatase (STS) transforms hormone precursors into active steroids. Thus, it represents a target of intense research regarding hormone-dependent cancers. In this study, three ligand-based pharmacophore models were developed to identify STS inhibitors from natural sources. In a pharmacophore-based virtual screening of a curated molecular TCM database, lanostane-type triterpenes (LTTs) were predicted as STS ligands. Three traditionally used polypores rich in LTTs, i.e., Ganoderma lucidum Karst., Gloeophyllum odoratum Imazeki, and Fomitopsis pinicola Karst., were selected as starting materials. Based on eighteen thereof isolated LTTs a structure activity relationship for this compound class was established with piptolinic acid D (1), pinicolic acid B (2), and ganoderol A (3) being the most pronounced and first natural product STS inhibitors with IC50 values between 10 and 16 µM. Molecular docking studies proposed crucial ligand target interactions and a prediction tool for these natural compounds correlating with experimental findings.

Abstract A136: Small molecule covalent drug discovery by IMTACTM

Abstract There is currently a resurgence of interest in developing covalent drugs due to the high potency, good selectivity, and prolonged effects which may results in less-frequent drug dosing and wider therapeutic windows for patients. BridGene Biosciences established a novel IMTACTM (Isobaric Mass Tagged Affinity Characterization) platform for covalent drug discovery. Using IMTACTM platform, an intelligently designed drug-like probe with alkyne tag is able to “fish out” and identify proteins covalently bound to probes from live cells which enables us to discover druggable opportunities across the entire proteome. There are several advantages of IMTACTM : 1. Discover ligands for unliganded or undruggable targets; 2. Reduce the time and cost of lead discovery; 3. Identify additional targets of drugs or clinical candidates; 4. Build ligand-target interaction database and guide drug design. Using IMTACTM platform, our covalent library is screened in various live cells including cancer immune, and neuronal cell lines. The hit rate of IMTAC screening is about 20%, and the targets discovered by IMTACTM includes GMPS, Rictor, BTK, FABP5, ALDH, FUS, MIF and etc. Given the drug-like probe design, the probes can have nanomolar potency on the targets. And the drug like probe design minimizes the SAR efforts of developing a hit to a drug. IMTACTM platform allows us to expand the therapeutic applications of small molecule drugs to a new horizon. Citation Format: Heather Ha, Robert Stanley, Qian Cai, Wendy Zhou, Hang Chen, Irene Yuan, Ping Cao. Small molecule covalent drug discovery by IMTACTM [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A136. doi:10.1158/1535-7163.TARG-19-A136

Solid-Supported Proteins in the Liquid Chromatography Domain to Probe Ligand-Target Interactions.

Ligand-target interactions play a central role in drug discovery processes because these interactions are crucial in biological systems. Small molecules-proteins interactions can regulate and modulate protein function and activity through conformational changes. Therefore, bioanalytical tools to screen new ligands have focused mainly on probing ligand-target interactions. These interactions have been evaluated by using solid-supported proteins, which provide advantages like increased protein stability and easier protein extraction from the reaction medium, which enables protein reuse. In some specific approaches, precisely in the ligand fishing assay, the bioanalytical method allows the ligands to be directly isolated from complex mixtures, including combinatorial libraries and natural products extracts without prior purification or fractionation steps. Most of these screening assays are based on liquid chromatography separation, and the binding events can be monitored through on-line or off-line methods. In the on-line approaches, solid supports containing the immobilized biological target are used as chromatographic columns most of the time. Several terms have been used to refer to such approaches, such as weak affinity chromatography, high-performance affinity chromatography, on-flow activity assays, and high-performance liquid affinity chromatography. On the other hand, in the off-line approaches, the binding event occurs outside the liquid chromatography system and may encompass affinity and activity-based assays in which the biological target is immobilized on magnetic particles or monolithic silica, among others. After the incubation step, the supernatant or the eluate from the binding assay is analyzed by liquid chromatography coupled to various detectors. Regardless of the selected bioanalytical approach, the use of solid supported proteins has significantly contributed to the development of automated and reliable screening methods that enable ligands to be isolated and characterized in complex matrixes without purification, thereby reducing costs and avoiding time-laborious steps. This review provides a critical overview of recently developed assays.

Advances in MS Based Strategies for Probing Ligand-Target Interactions: Focus on Soft Ionization Mass Spectrometric Techniques.

The non-covalent interactions between small drug molecules and disease-related proteins (ligand-target interactions) mediate various pharmacological processes in the treatment of different diseases. The development of the analytical methods to assess those interactions, including binding sites, binding energies, stoichiometry and association-dissociation constants, could assist in clarifying the mechanisms of action, precise treatment of targeted diseases as well as the targeted drug discovery. For the last decades, mass spectrometry (MS) has been recognized as a powerful tool to study the non-covalent interactions of the ligand-target complexes with the characteristics of high sensitivity, high-resolution, and high-throughput. Soft ionization mass spectrometry, especially the electrospray mass spectrometry (ESI-MS) and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS), could achieve the complete transformation of the target analytes into the gas phase, and subsequent detection of the small drug molecules and disease-related protein complexes, and has exerted great advantages for studying the drug ligands-protein targets interactions, even in case of identifying active components as drug ligands from crude extracts of medicinal plants. Despite of other analytical techniques for this purpose, such as the NMR and X-ray crystallography, this review highlights the principles, research hotspots and recent applications of the soft ionization mass spectrometry and its hyphenated techniques, including hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking mass spectrometry (CX-MS), and ion mobility spectrometry mass spectrometry (IMS-MS), in the study of the non-covalent interactions between small drug molecules and disease-related proteins.

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-autoencoder to learn fixed-size representations of protein pockets from a set of representative druggable protein binding sites. Second, we trained two Graph-CNNs to automatically extract features from pocket graphs and 2D ligand graphs, respectively, driven by binding classification labels. We demonstrate that graph-autoencoders can learn fixed-size representations for protein pockets of varying sizes and the Graph-CNN framework can effectively capture protein-ligand binding interactions without relying on target-ligand complexes. Across several metrics, Graph-CNNs achieved better or comparable performance to 3DCNN ligand-scoring, AutoDock Vina, RF-Score, and NNScore on common virtual screening benchmark data sets. Visualization of key pocket residues and ligand atoms contributing to the classification decisions confirms that our networks are able to detect important interface residues and ligand atoms within the pockets and ligands, respectively.

Synthesis and Inhibition Evaluation of New Benzyltetrahydroprotoberberine Alkaloids Designed as Acetylcholinesterase Inhibitors.

Secondary metabolites from natural products are a potential source of acetylcholinesterase inhibitors (AChEIs), which is a key enzyme in the treatment of many neurodegenerative diseases. Inspired by the reported activities of isoquinoline-derivative alkaloids herein we report the design, one step synthesis and evaluation by capillary enzyme reactor (ICER) of benzyl analogs (1a–1e) of the tetrahydroprotoberberine alkaloid stepholidine, which is abundant in Onychopetalum amazonicum. Docking analysis based on the crystal structure of Torpedo californica AChE (TcAChE) indicated that π-π interactions were dominant in all planned derivatives and that the residues from esteratic, anionic and peripheral subsites of the enzyme played key interaction roles. Due to the similarities observed when compared with galantamine in the AChE complex, the results suggest that ligand-target interactions would increase, especially for the N-benzyl derivatives. From a series of synthesized compounds, the alkaloids (7R,13aS)-7-benzylstepholidine (1a), (7S,13aS)-7-benzylstepholidine (1b), and (S)-10-O-benzylstepholidine (1d) are reported here for the first time. The on flow bioaffinity chromatography inhibition assay, based on the quantification of choline, revealed the N-benzylated compound 1a and its epimer 1b to be the most active, with IC50 of 40.6 ± 1 and 51.9 ± 1 μM, respectively, and a non-competitive mechanism. The proposed approach, which is based on molecular docking and bioaffinity chromatography, demonstrated the usefulness of stepholidine as a template for the design of rational AChEIs and showed how the target-alkaloid derivatives interact with AChE.

Molecular Docking: Shifting Paradigms in Drug Discovery.

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Probing the Ligand-Binding Pocket of BTN3A1.

Small-molecule phosphoantigens such as (E)-4-hydroxy-3-methyl-but-2-enyl diphosphate stimulate human Vγ9Vδ2 T cells after binding to the intracellular B30.2 domain of the immune receptor butyrophilin 3 isoform A1 (BTN3A1). To understand the ligand-target interaction in greater detail, we performed molecular docking. Based on the docking results, we synthesized the novel ligand (E)-(7-hydroxy-6-methylhept-5-en-1-yl)phosphonate and mutated proposed binding site residues. We evaluated the impact on butyrophilin binding of existing and novel ligands using a newly developed high-throughput fluorescence polarization assay. We also evaluated the ability of the compounds to stimulate proliferation and interferon-γ production of Vγ9Vδ2 T cells. Mutation of H381 fully blocked ligand binding, whereas mutations to charged surface residues impacted diphosphate interactions. Monophosphonate analogs bind similarly to BTN3A1, although they differ in their antigenicity, demonstrating that binding and efficacy are not linearly correlated. These results further define the structure-activity relationships underlying BTN3A1 ligand binding and antigenicity and support further structure-guided drug design.

Large-Scale Target Identification of Herbal Medicine Using a Reverse Docking Approach.

Herbal medicine has been used to countermine various diseases for centuries. However, most of the therapeutic targets underlying herbal therapy remain unclear, which largely slow down the novel drug discovery process from natural products. In this study, we developed a novel computational pipeline for assisting de novo identification of protein targets for herbal ingredients. The pipeline involves pharmacophore comparison and reverse ligand–protein docking simulation in a high throughput manner. We evaluated the pipeline using three traditional Chinese medicine ingredients such as acteoside, quercetin, and epigallocatechin gallate as examples. A majority of current known targets of these ingredients were successfully identified by the pipeline. Structural comparative analyses confirmed that the predicted ligand–target interactions used the same binding pockets and binding modes as those of known ligand–target interactions. Furthermore, we illustrated the mechanism of actions of the ingredients by constructing the pharmacological networks on the basis of the predicted target profiles. In summary, we proposed an efficient and economic option for large-scale target exploration in the herb study. This pipeline will be particularly valuable in aiding precise drug discovery and drug repurposing from natural products.

In silico exploration the phenolic compound of olive leaves as acetylcholinesterase enzyme (AChE) inhibitor for Alzheimer’s disease therapy

Olive (Olea europaea) have been cultivated and grown well in tropical climates such as Indonesia. Indonesia local community have used olive as herbal medicines due to its active compounds known as oleuropein that has many biological activities including as neurotherapy in Alzheimer's disease. The purpose of this study is to explore the potential phenolic compounds of olive and examine the acetylcholinesterase (AChE) inhibitory activity displayed by different olive polyphenols through a silico approach. The bioactive compounds of olive which had been analyzed in this study were phenolic compound included oleuropein, demethyl-oleuropein, ligstroside, oleoside, verbascoside, luteolin 7-glucoside, and hydroxytyrosol. Interaction of bioactive compounds with acetylcholinesterase (AChE) was analyzed through molecular specific docking using AutoDock Vina with Pyrx Software. The result elucidate that olive contain potential biological activities as antioxidant, anti-inflammatory, antineoplastic, free radical scavenger, antibacterial, antifungal, expression TP53 enhancer, caspase 8 stimulant, platelet adhesion inhibitor, treatment for lipoprotein disorder, antiviral and dementia treatment/Alzheimer disease. The highest bioactivity percentage of olive phenolic compound are as an antioxidant of 82%, anti-inflammatory of 73%, and anti-cancer (antineoplastic) of 70% respectively. Based on molecular docking analysis show one of olive phenolic compound of the dimethyl-oleuropein has strong interaction with AChE as pointed in the binding affinity of demethyl-oleuropein +AChE of -8.9 kcal/mol has closed to galanthamine binding affinity of -10.3 kcal/mol. Along with glutamate acid 202, tyrosine 133 and tyrosine 124 are the major contributors in the target-ligand interactions. The selected demethyl-oleuropein ought to be tested in clinical studies to discover new neuro-therapeutic candidates.

New computational studies to support cyclin-dependent kinase 9 inhibitor screening and design

Aims: Cyclin-dependent kinase 9 (CDK9) plays a major role in the regulation of transcription. Its overexpression -which occurs in several types of cancer- increases the levels of certain antiapoptotic proteins that can lead to tumorigenesis, therefore the identification of new, more potent and more selective inhibitors is essential.
 Methods: In this study we present a computational approach, which can facilitate lead selection and optimization.
 Results: First, a pharmacophore hypothesis based on the active compounds has been developed to identify the key features for the ligand-target interaction. This was followed by the docking of the compounds into the active site of CDK9, the poses and interactions with the amino acids were compared with those of the co-crystallized ligand. The mode of their binding further explained the characteristics of these inhibitors while the docking scores can be a factor in the selection of active compounds in the future. Finally, a field-based QSAR model was also created, to predict the activity of inhibitor candidates.
 Conclusion: With our current work we deepened our knowledge about the interactions between CDK9 and its inhibitors, which can contribute to the discovery of novel CDK9 inhibitors.

A Comparative Analysis of the Molecular Interaction Techniques for In Silico Drug Design

Computer Aided Drug Discovery (CADD) has become a vital tool for the rapid identification of new candidate molecules prior to their chemical synthesis and screening. For precarious lifestyle diseases such as various types of carcinoma, CADD offers a fast and cost-effective approach for development of therapeutic drugs. Advancement in the molecular modelling systems enabled easier identification of the target, along with visualisation and interpretation of target-ligand interactions. This review highlights in silico drug design and development strategies with detailed description of the various approaches like classical variable selection and artificial intelligence which are used to select molecular descriptors. This study also critically evaluates the different molecular docking approaches like SLIDE, GLIDE, FlexX-Pharm, GOLD, AutoDock, FRED and the more frequently used ones like HADDOCK, AutoDock Vina and UCSF DOCK for anti-cancer drug development. Since cancer is a heterogeneous disease drug discovery becomes excessively complex. Bioinformatics therefore play a major role in a step wise drug discovery process in a lesser time and a cost efficient manner.

Recent Applications of Diazirines in Chemical Proteomics.

The elucidation of substrate-protein interactions is an important component of the drug development process. Due to the complexity of native cellular environments, elucidating these fundamental biochemical interactions remains challenging. Photoaffinity labeling (PAL) is a versatile technique that can provide insight into ligand-target interactions. By judicious modification of substrates with a photoreactive group, PAL creates a covalent crosslink between a substrate and its biological target following UV-irradiation. Among the commonly employed photoreactive groups, diazirines have emerged as the gold standard. In this Minireview, recent developments in the field of diazirine-based photoaffinity labeling will be discussed, with emphasis being placed on their applications in chemical proteomic studies.