Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures.

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

In Silico and In Vitro Identification of P-Glycoprotein Inhibitors from a Library of 375 Phytochemicals.

Cancer therapy with clinically established anticancer drugs is frequently hampered by the development of drug resistance of tumors and severe side effects in normal organs and tissues. The demand for powerful, but less toxic, drugs is high. Phytochemicals represent an important reservoir for drug development and frequently exert less toxicity than synthetic drugs. Bioinformatics can accelerate and simplify the highly complex, time-consuming, and expensive drug development process. Here, we analyzed 375 phytochemicals using virtual screenings, molecular docking, and in silico toxicity predictions. Based on these in silico studies, six candidate compounds were further investigated in vitro. Resazurin assays were performed to determine the growth-inhibitory effects towards wild-type CCRF-CEM leukemia cells and their multidrug-resistant, P-glycoprotein (P-gp)-overexpressing subline, CEM/ADR5000. Flow cytometry was used to measure the potential to measure P-gp-mediated doxorubicin transport. Bidwillon A, neobavaisoflavone, coptisine, and z-guggulsterone all showed growth-inhibitory effects and moderate P-gp inhibition, whereas miltirone and chamazulene strongly inhibited tumor cell growth and strongly increased intracellular doxorubicin uptake. Bidwillon A and miltirone were selected for molecular docking to wildtype and mutated P-gp forms in closed and open conformations. The P-gp homology models harbored clinically relevant mutations, i.e., six single missense mutations (F336Y, A718C, Q725A, F728A, M949C, Y953C), three double mutations (Y310A-F728A; F343C-V982C; Y953A-F978A), or one quadruple mutation (Y307C-F728A-Y953A-F978A). The mutants did not show major differences in binding energies compared to wildtypes. Closed P-gp forms generally showed higher binding affinities than open ones. Closed conformations might stabilize the binding, thereby leading to higher binding affinities, while open conformations may favor the release of compounds into the extracellular space. In conclusion, this study described the capability of selected phytochemicals to overcome multidrug resistance.

Coronavirus Inhibitors Targeting nsp16.

During the past three decades, humans have been confronted with different new coronavirus outbreaks. Since the end of the year 2019, COVID-19 threatens the world as a rapidly spreading infectious disease. For this work, we targeted the non-structural protein 16 (nsp16) as a key protein of SARS-CoV-2, SARS-CoV-1 and MERS-CoV to develop broad-spectrum inhibitors of nsp16. Computational methods were used to filter candidates from a natural product-based library of 224,205 compounds obtained from the ZINC database. The binding of the candidates to nsp16 was assessed using virtual screening with VINA LC, and molecular docking with AutoDock 4.2.6. The top 9 compounds were bound to the nsp16 protein of SARS-CoV-2, SARS-CoV-1, and MERS-CoV with the lowest binding energies (LBEs) in the range of -9.0 to -13.0 kcal with VINA LC. The AutoDock-based LBEs for nsp16 of SARS-CoV-2 ranged from -11.42 to -16.11 kcal/mol with predicted inhibition constants (pKi) from 0.002 to 4.51 nM, the natural substrate S-adenosyl methionine (SAM) was used as control. In silico results were verified by microscale thermophoresis as in vitro assay. The candidates were investigated further for their cytotoxicity in normal MRC-5 lung fibroblasts to determine their therapeutic indices. Here, the IC50 values of all three compounds were >10 µM. In summary, we identified three novel SARS-CoV-2 inhibitors, two of which showed broad-spectrum activity to nsp16 in SARS-CoV-2, SARS-CoV-1, and MERS-CoV. All three compounds are coumarin derivatives that contain chromen-2-one in their scaffolds.

In Silico and In Vitro Screening of 50 Curcumin Compounds as EGFR and NF-κB Inhibitors.

The improvement of cancer chemotherapy remains a major challenge, and thus new drugs are urgently required to develop new treatment regimes. Curcumin, a polyphenolic antioxidant derived from the rhizome of turmeric (Curcuma longa L.), has undergone extensive preclinical investigations and, thereby, displayed remarkable efficacy in vitro and in vivo against cancer and other disorders. However, pharmacological limitations of curcumin stimulated the synthesis of numerous novel curcumin analogs, which need to be evaluated for their therapeutic potential. In the present study, we calculated the binding affinities of 50 curcumin derivatives to known cancer-related target proteins of curcumin, i.e., epidermal growth factor receptor (EGFR) and nuclear factor κB (NF-κB) by using a molecular docking approach. The binding energies for EGFR were in a range of −12.12 (±0.21) to −7.34 (±0.07) kcal/mol and those for NF-κB ranged from −12.97 (±0.47) to −6.24 (±0.06) kcal/mol, indicating similar binding affinities of the curcumin compounds for both target proteins. The predicted receptor-ligand binding constants for EGFR and curcumin derivatives were in a range of 0.00013 (±0.00006) to 3.45 (±0.10) µM and for NF-κB in a range of 0.0004 (±0.0003) to 10.05 (±4.03) µM, indicating that the receptor-ligand binding was more stable for EGFR than for NF-κB. Twenty out of 50 curcumin compounds showed binding energies to NF-κB smaller than −10 kcal/mol, while curcumin as a lead compound revealed free binding energies of >−10 kcal/mol. Comparable data were obtained for EGFR: 15 out of 50 curcumin compounds were bound to EGFR with free binding energies of <−10 kcal/mol, while the binding affinity of curcumin itself was >−10 kcal/mol. This indicates that the derivatization of curcumin may indeed be a promising strategy to improve targe specificity and to obtain more effective anticancer drug candidates. The in silico results have been exemplarily validated using microscale thermophoresis. The bioactivity has been further investigated by using resazurin cell viability assay, lactate dehydrogenase assay, flow cytometric measurement of reactive oxygen species, and annexin V/propidium iodide assay. In conclusion, molecular docking represents a valuable approach to facilitate and speed up the identification of novel targeted curcumin-based drugs to treat cancer.

A novel ligand of the translationally controlled tumor protein (TCTP) identified by virtual drug screening for cancer differentiation therapy.

Introduction Differentiation therapy is a promising strategy for cancer treatment. The translationally controlled tumor protein (TCTP) is an encouraging target in this context. By now, this field of research is still at its infancy, which motivated us to perform a large-scale screening for the identification of novel ligands of TCTP. We studied the binding mode and the effect of TCTP blockade on the cell cycle in different cancer cell lines. Methods Based on the ZINC-database, we performed virtual screening of 2,556,750 compounds to analyze the binding of small molecules to TCTP. The in silico results were confirmed by microscale thermophoresis. The effect of the new ligand molecules was investigated on cancer cell survival, flow cytometric cell cycle analysis and protein expression by Western blotting and co-immunoprecipitation in MOLT-4, MDA-MB-231, SK-OV-3 and MCF-7 cells. Results Large-scale virtual screening by PyRx combined with molecular docking by AutoDock4 revealed five candidate compounds. By microscale thermophoresis, ZINC10157406 (6-(4-fluorophenyl)-2-[(8-methoxy-4-methyl-2-quinazolinyl)amino]-4(3H)-pyrimidinone) was identified as TCTP ligand with a KD of 0.87 ± 0.38. ZINC10157406 revealed growth inhibitory effects and caused G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. ZINC10157406 (2 × IC50) downregulated TCTP expression by 86.70 ± 0.44% and upregulated p53 expression by 177.60 ± 12.46%. We validated ZINC10157406 binding to the p53 interaction site of TCTP and replacing p53 by co-immunoprecipitation. Discussion ZINC10157406 was identified as potent ligand of TCTP by in silico and in vitro methods. The compound bound to TCTP with a considerably higher affinity compared to artesunate as known TCTP inhibitor. We were able to demonstrate the effect of TCTP blockade at the p53 binding site, i.e. expression of TCTP decreased, whereas p53 expression increased. This effect was accompanied by a dose-dependent decrease of CDK2, CDK4, CDK, cyclin D1 and cyclin D3 causing a G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. Our findings are supposed to stimulate further research on TCTP-specific small molecules for differentiation therapy in oncology.

Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma.

Background Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.

In Silico Mining of Terpenes from Red-Sea Invertebrates for SARS-CoV-2 Main Protease (Mpro) Inhibitors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19 pandemic, which generated more than 1.82 million deaths in 2020 alone, in addition to 83.8 million infections. Currently, there is no antiviral medication to treat COVID-19. In the search for drug leads, marine-derived metabolites are reported here as prospective SARS-CoV-2 inhibitors. Two hundred and twenty-seven terpene natural products isolated from the biodiverse Red-Sea ecosystem were screened for inhibitor activity against the SARS-CoV-2 main protease (Mpro) using molecular docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area binding energy calculations. On the basis of in silico analyses, six terpenes demonstrated high potency as Mpro inhibitors with ΔGbinding ≤ -40.0 kcal/mol. The stability and binding affinity of the most potent metabolite, erylosides B, were compared to the human immunodeficiency virus protease inhibitor, lopinavir. Erylosides B showed greater binding affinity towards SARS-CoV-2 Mpro than lopinavir over 100 ns with ΔGbinding values of -51.9 vs. -33.6 kcal/mol, respectively. Protein-protein interactions indicate that erylosides B biochemical signaling shares gene components that mediate severe acute respiratory syndrome diseases, including the cytokine- and immune-signaling components BCL2L1, IL2, and PRKC. Pathway enrichment analysis and Boolean network modeling were performed towards a deep dissection and mining of the erylosides B target-function interactions. The current study identifies erylosides B as a promising anti-COVID-19 drug lead that warrants further in vitro and in vivo testing.

Inhibition of cell migration and induction of apoptosis by a novel class II histone deacetylase inhibitor, MCC2344.

Epigenetic modifiers provide a new target for the development of anti-cancer drugs. The eraser histone deacetylase 6 (HDAC6) is a class IIb histone deacetylase that targets various non-histone proteins such as transcription factors, nuclear receptors, cytoskeletal proteins, DNA repair proteins, and molecular chaperones. Therefore, it became an attractive target for cancer treatment. In this study, virtual screening was applied to the MicroCombiChem database with 1162 drug-like compounds to identify new HDAC6 inhibitors. Five compounds were tested in silico and in vitro as HDAC6 inhibitors. Both analyses revealed 1-cyclohexene-1-carboxamide, 2-hydroxy-4,4-dimethyl-N-1-naphthalenyl-6-oxo- (MCC2344) as the best HDAC6 inhibitor among the five ligands. The binding affinity of MCC2344 to HDAC6 was further confirmed by microscale thermophoresis. Additionally, the anti-cancer activity of MCC2344 was tested in several tumor cell lines. Leukemia cells were the most sensitive cells towards MCC2344, particularly the P-glycoprotein-overexpressing multidrug-resistant cell line CEM/ADR5000 exhibited remarkable collateral sensitivity towards MCC2344. Transcriptome analysis using microarray hybridization was performed for investigating downstream mechanisms of action of MCC2344 in leukemia cells. MCC2344 affected microtubule dynamics and suppressed cell migration in the wound healing assay as well as in a spheroid model by hyper-acetylation of tubulin and HSP-90. MCC2344 induced cell death in CEM/ADR5000 cells by activation of PARP, caspase-3, and p21 in addition to the downregulation of p62. MCC2344 significantly inhibited tumor growth in vivo in zebrafish larvae without mortality until 20 pM. We propose MCC2344 as a novel HDAC6 inhibitor for cancer treatment.

A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC50 of the target compounds. The performance of the models was assessed primarily through analysis of the Q2 values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.

Congestion game scheduling for virtual drug screening optimization.

In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

High performance in silico virtual drug screening on many-core processors.

Drug screening is an important part of the drug development pipeline for the pharmaceutical industry. Traditional, lab-based methods are increasingly being augmented with computational methods, ranging from simple molecular similarity searches through more complex pharmacophore matching to more computationally intensive approaches, such as molecular docking. The latter simulates the binding of drug molecules to their targets, typically protein molecules. In this work, we describe BUDE, the Bristol University Docking Engine, which has been ported to the OpenCL industry standard parallel programming language in order to exploit the performance of modern many-core processors. Our highly optimized OpenCL implementation of BUDE sustains 1.43 TFLOP/s on a single Nvidia GTX 680 GPU, or 46% of peak performance. BUDE also exploits OpenCL to deliver effective performance portability across a broad spectrum of different computer architectures from different vendors, including GPUs from Nvidia and AMD, Intel's Xeon Phi and multi-core CPUs with SIMD instruction sets.

Identification of Antagonistic Action of Pyrrolizidine Alkaloids in Muscarinic Acetylcholine Receptor M1 by Computational Target Prediction Analysis.

Pyrrolizidine alkaloids (PAs) are one of the largest distributed classes of toxins in nature. They have a wide range of toxicity, such as hepatotoxicity, pulmonary toxicity, neuronal toxicity, and carcinogenesis. Yet, biological targets responsible for these effects are not well addressed. Using methods of computational biology for target identification, we tested more than 200 PAs. We used a machine-learning approach that applies structural similarity for target identification, ChemMapper, and SwissTargetPrediction. The predicted target with high probability was muscarinic acetylcholine receptor M1. The predicted interactions between this target and PAs were further studied by molecular docking-based binding energies using AutoDock and VinaLC, which revealed good binding affinities. The PAs are bound to the same binding pocket as pirenzepine, a known M1 antagonist. These results were confirmed by in vitro assays showing that PAs increased the levels of intracellular calcium. We conclude that PAs are potential acetylcholine receptor M1 antagonists. This elucidates for the first time the serious neuro-oncological toxicities exerted by PA consumption.

Binding of the repressor complex REST-mSIN3b by small molecules restores neuronal gene transcription in Huntington's disease models.

Transcriptional dysregulation is a hallmark of Huntington's disease (HD) and one cause of this dysregulation is enhanced activity of the REST-mSIN3a-mSIN3b-CoREST-HDAC repressor complex, which silences transcription through REST binding to the RE1/NRSE silencer. Normally, huntingtin (HTT) prevents this binding, allowing expressing of REST target genes. Here, we aimed to identify HTT mimetics that disrupt REST complex formation in HD. From a structure-based virtual screening of 7 million molecules, we selected 94 compounds predicted to interfere with REST complex formation by targeting the PAH1 domain of mSIN3b. Primary screening using DiaNRSELuc8 cells revealed two classes of compounds causing a greater than two-fold increase in luciferase. In particular, quinolone-like compound 91 (C91) at a non-toxic nanomolar concentration reduced mSIN3b nuclear entry and occupancy at the RE1/NRSE within the Bdnf locus, and restored brain-derived neurotrophic factor (BDNF) protein levels in HD cells. The mRNA levels of other RE1/NRSE-regulated genes were similarly increased while non-REST-regulated genes were unaffected. C91 stimulated REST-regulated gene expression in HTT-knockdown Zebrafish and increased BDNF mRNA in the presence of mutant HTT. Thus, a combination of virtual screening and biological approaches can lead to compounds reducing REST complex formation, which may be useful in HD and in other pathological conditions.

FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding.

Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments. In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI. Electronic Supplementary Material: Supplementary material is available for this article at 10.1007/s11704-022-2163-9 and is accessible for authorized users.

Virtual Screening-Based Study of Novel Anti-Cancer Drugs Targeting G-Quadruplex.

In order to develop new anti-cancer drugs more efficiently and reduce side effects based on active drug targets, the virtual drug screening was carried out through the target of G-quadruplexes and 23 hit compounds were, thus, screened out as potential anticancer drugs. Six classical G-quadruplex complexes were introduced as query molecules, and the three-dimensional similarity of molecules was calculated by shape feature similarity (SHAFTS) method so as to reduce the range of potential compounds. Afterwards, the molecular docking technology was utilized to perform the final screening followed by the exploration of the binding between each compound and four different structures of G-quadruplex. In order to verify the anticancer activity of the selected compounds, compounds 1, 6 and 7 were chosen to treat A549 cells in vitro, the lung cancer epithelial cells, for further exploring their anticancer activity. These three compounds were found to be of good characteristics in the treatment of cancer, which revealed the great application prospect of the virtual screening method in developing new drugs.

A Computational Software for Training Robust Drug-Target Affinity Prediction Models: pydebiaseddta.

Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta: a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates modifying the training distribution to mitigate the effect of spurious correlations in the training data set that leads to substantially degraded performance for novel ligands and proteins. Written in Python programming language, pydebiaseddta combines a user-friendly streamlined interface with a feature-rich and highly modifiable architecture. With this article we introduce our software, showcase its main functionalities, and describe practical ways for new users to engage with it.

A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models.

Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships. This leads the prediction performances of these models to drop dramatically for previously unseen biomolecules. Various approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading overall prediction performance. In this article, we present DebiasedDTA, a novel training framework for drug-target affinity (DTA) prediction models that addresses data set biases to improve the generalizability of such models. DebiasedDTA relies on reweighting the training samples to achieve robust generalization, and is thus applicable to most DTA prediction models. Extensive experiments with different biomolecule representations, model architectures, and data sets demonstrate that DebiasedDTA achieves improved generalizability in predicting drug-target affinities.

In Silico and In Vitro Identification of Pan-Coronaviral Main Protease Inhibitors from a Large Natural Product Library.

The main protease (Mpro or 3CLpro) in coronaviruses represents a promising specific drug target as it is essential for the cleavage of the virus polypeptide and has a unique cleavage site that does not exist in human host proteases. In this study, we explored potential natural pan-coronavirus drugs using in vitro and in silico approaches and three coronavirus main proteases as treatment targets. The PyRx program was used to screen 39,442 natural-product-like compounds from the ZINC database and 121 preselected phytochemicals from medicinal plants with known antiviral activity. After assessment with Lipinski's rule of five, molecular docking was performed for the top 33 compounds of both libraries. Enzymatic assays were applied for the top candidates from both in silico approaches to test their ability to inhibit SARS-CoV-2 Mpro. The four compounds (hypericin, rosmarinic acid, isorhamnetin, and luteolin) that most efficiently inhibited SARS-CoV-2 Mpro in vitro were further tested for their efficacy in inhibiting Mpro of SARS-CoV-1 and MERS-CoV. Microscale thermophoresis was performed to determine dissociation constant (Kd) values to validate the binding of these active compounds to recombinant Mpro proteins of SARS-CoV-2, SARS-CoV-1, and MERS-CoV. The cytotoxicity of hypericin, rosmarinic acid, isorhamnetin, and luteolin was assessed in human diploid MRC-5 lung fibroblasts using the resazurin cell viability assay to determine their therapeutic indices. Sequence alignment of Mpro of SARS-CoV-2 demonstrated 96.08%, 50.83%, 49.17%, 48.51%, 44.04%, and 41.06% similarity to Mpro of other human-pathogenic coronaviruses (SARS-CoV-1, MERS-CoV, HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E, respectively). Molecular docking showed that 12 out of 121 compounds were bound to SARS-CoV-2 Mpro at the same binding site as the control inhibitor, GC376. Enzyme inhibition assays revealed that hypericin, rosmarinic acid, isorhamnetin, and luteolin inhibited Mpro of SARS-CoV-2, while hypericin and isorhamnetin inhibited Mpro of SARS-CoV-1; hypericin showed inhibitory effects toward Mpro of MERS-CoV. Microscale thermophoresis confirmed the binding of these compounds to Mpro with high affinity. Resazurin assays showed that rosmarinic acid and luteolin were not cytotoxic toward MRC-5 cells, whereas hypericin and isorhamnetin were slightly cytotoxic. We demonstrated that hypericin represents a potential novel pan-anti-coronaviral agent by binding to and inhibiting Mpro of several human-pathogenic coronaviruses. Moreover, isorhamnetin showed inhibitory effects toward SARS-CoV-2 and SARS-CoV-1 Mpro, indicating that this compound may have some pan-coronaviral potential. Luteolin had inhibitory effects against SARS-CoV-2 Mpro.

Potential Coronaviral Inhibitors of the Nucleocapsid Protein Identified In Silico and In Vitro from a Large Natural Product Library.

The nucleocapsid protein (NP) is one of the main proteins out of four structural proteins of coronaviruses including the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, discovered in 2019. NP packages the viral RNA during virus assembly and is, therefore, indispensable for virus reproduction. NP consists of two domains, i.e., the N- and C-terminal domains. RNA-binding is mainly performed by a binding pocket within the N-terminal domain (NTD). NP represents an important target for drug discovery to treat COVID-19. In this project, we used the Vina LC virtual drug screening software and a ZINC-based database with 210,541 natural and naturally derived compounds that specifically target the binding pocket of NTD of NP. Our aim was to identify coronaviral inhibitors that target NP not only of SARS-CoV-2 but also of other diverse human pathogenic coronaviruses. Virtual drug screening and molecular docking procedures resulted in 73 candidate compounds with a binding affinity below -9 kcal/mol with NP NTD of SARS-CoV-1, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-NL63, HoC-229E, and HCoV-HKU1. The top five compounds that met the applied drug-likeness criteria were then tested for their binding in vitro to the NTD of the full-length recombinant NP proteins using microscale thermophoresis. Compounds (1), (2), and (4), which belong to the same scaffold family of 4-oxo-substituted-6-[2-(4a-hydroxy-decahydroisoquinolin-2-yl)2H-chromen-2-ones and which are derivates of coumarin, were bound with good affinity to NP. Compounds (1) and (4) were bound to the full-length NP of SARS-CoV-2 (aa 1-419) with Kd values of 0.798 (±0.02) µM and 8.07 (±0.36) µM, respectively. Then, these coumarin derivatives were tested with the SARS-CoV-2 NP NTD (aa 48-174). Compounds (1) and (4) revealed Kd-values of 0.95 (±0.32) µM and 7.77 (±6.39) µM, respectively. Compounds (1) and (4) caused low toxicity in human A549 and MRC-5 cell lines. These compounds may represent possible drug candidates, which need further optimization to be used against COVID-19 and other coronaviral infections.

Seeking the exclusive binding region of phenylalkylamine derivatives on human T-type calcium channels via homology modeling and molecular dynamics simulation approach.

Pharmaceutical features of phenylalkylamine derivatives (PAAs) binding to calcium channels have been studied extensively in the past decades. Only a few PAAs have the binding specificity on calcium channels, for example, NNC 55-0396. Here, we created the homology models of human Cav 3.2, Cav 3.3 and use them as a receptor on the rigid docking tests. The nonspecific calcium channel blocker mibefradil showed inconsistent docking preference across four domains; however, NNC 55-0396 had a unique binding pattern on domain II specifically. The subsequent molecular dynamics (MD) simulations identified that Cav 3.1, Cav 3.2, and Cav 3.3 share domain II when Ca2+ appearing in the neighbor region of selective filters (SFs). Moreover, free-energy perturbation analysis suggests single mutation of lysine at P-loop domain III, or threonine at the P-loop domain II largely reduced the total amount of hydration-free energy in the system. All these findings suggest that P-loop and segment six domain II in the T-type calcium channels (TCCs) are crucial for attracting the PAAs with specificity as the antagonist.