Drug repurposing screen to identify inhibitors of the RNA polymerase (nsp12) and helicase (nsp13) from SARS-CoV-2 replication and transcription complex

Coronaviruses contain one of the largest genomes among the RNA viruses, coding for 14-16 non-structural proteins (nsp) that are involved in proteolytic processing, genome replication and transcription, and four structural proteins that build the core of the mature virion. Due to conservation across coronaviruses, nsps form a group of promising drug targets as their inhibition directly affects viral replication and, therefore, progression of infection. A minimal but fully functional replication and transcription complex was shown to be formed by one RNA-dependent RNA polymerase (nsp12), one nsp7, two nsp8 accessory subunits, and two helicase (nsp13) enzymes. Our approach involved, targeting nsp12 and nsp13 to allow multiple starting point to interfere with virus infection progression. Here we report a combined in-vitro repurposing screening approach, identifying new and confirming reported SARS-CoV-2 nsp12 and nsp13 inhibitors.

Design, quality and validation of the EU-OPENSCREEN fragment library poised to a high-throughput screening collection

The EU-OPENSCREEN (EU-OS) European Research Infrastructure Consortium (ERIC) is a multinational, not-for-profit initiative that integrates high-capacity screening platforms and chemistry groups across Europe to facilitate research in chemical biology and early drug discovery. Over the years, the EU-OS has assembled a high-throughput screening compound collection, the European Chemical Biology Library (ECBL), that contains approximately 100 000 commercially available small molecules and a growing number of thousands of academic compounds crowdsourced through our network of European and non-European chemists. As an extension of the ECBL, here we describe the computational design, quality control and use case screenings of the European Fragment Screening Library (EFSL) composed of 1056 mini and small chemical fragments selected from a substructure analysis of the ECBL. Access to the EFSL is open to researchers from both academia and industry. Using EFSL, eight fragment screening campaigns using different structural and biophysical methods have successfully identified fragment hits in the last two years. As one of the highlighted projects for antibiotics, we describe the screening by Bio-Layer Interferometry (BLI) of the EFSL, the identification of a 35 μM fragment hit targeting the beta-ketoacyl-ACP synthase 2 (FabF), its binding confirmation to the protein by X-ray crystallography (PDB 8PJ0), its subsequent rapid exploration of its surrounding chemical space through hit-picking of ECBL compounds that contain the fragment hit as a core substructure, and the final binding confirmation of two follow-up hits by X-ray crystallography (PDB 8R0I and 8R1V).

Outline and background for the EU-OS solubility prediction challenge

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility. The aim of the competition was multiple: from a practical side, the winning model should then serve as a cornerstone for future solubility predictions having used the largest training set so far publicly available. From a higher project perspective, the intent was to focus the energies and experiences, even if professionally not precisely coming from Pharma R&D; to address the issue of how to predict compound solubility. Here we report how the competition was ideated and the practical aspects of conducting it within the Kaggle framework, leveraging of the versatility and the open-source nature of this data science platform. Consideration on results and challenges encountered have been also examined.

Unexpected Single-Ligand Occupancy and Negative Cooperativity in the SARS-CoV-2 Main Protease

Many homodimeric enzymes tune their functions by exploiting either negative or positive cooperativity between subunits. In the SARS-CoV-2 Main protease (Mpro) homodimer, the latter has been suggested by symmetry in most of the 500 reported protease/ligand complex structures solved by macromolecular crystallography (MX). Here we apply the latter to both covalent and noncovalent ligands in complex with Mpro. Strikingly, our experiments show that the occupation of both active sites of the dimer originates from an excess of ligands. Indeed, cocrystals obtained using a 1:1 ligand/protomer stoichiometry lead to single occupation only. The empty binding site exhibits a catalytically inactive geometry in solution, as suggested by molecular dynamics simulations. Thus, Mpro operates through negative cooperativity with the asymmetric activity of the catalytic sites. This allows it to function with a wide range of substrate concentrations, making it resistant to saturation and potentially difficult to shut down, all properties advantageous for the virus' adaptability and resistance.

Combination of the biomarkers for aging and cancer? - Challenges and current status

The proportion of patients diagnosed with cancer has been shown to rise with the increasing aging global population. Advanced age is a major risk factor for morbidity and mortality in older adults. As individuals experience varying health statuses, particularly with age, it poses a challenge for medical professionals in the cancer field to obtain standardized treatment outcomes. Hence, relying solely on chronological age and disease-related parameters is inadequate for clinical decision-making for elderly patients. With functional, multimorbidity-related, and psychosocial changes that occur with aging, oncologic diseases may develop and be treated differently from younger patients, leading to unique challenges in treatment efficacy and tolerance. To overcome this challenge, personalized therapy using biomarkers has emerged as a promising solution. Various categories of biomarkers, including inflammatory, hematological, metabolic, endocrine, and DNA modification-related indicators, may display features related to both cancer and aging, aiding in the development of innovative therapeutic approaches for patients with cancer in old age. Furthermore, physical functional measurements as non-molecular phenotypic biomarkers are being investigated for their potential complementary role in structured multidomain strategies to combat age-related diseases such as cancer. This review provides insight into the current developments, recent discoveries, and significant challenges in cancer and aging biomarkers, with a specific focus on their application in advanced age.

Drug repurposing for the treatment of COVID-19: Targeting nafamostat to the lungs by a liposomal delivery system

Despite tremendous global efforts since the beginning of the COVID-19 pandemic, still only a limited number of prophylactic and therapeutic options are available. Although vaccination is the most effective measure in preventing morbidity and mortality, there is a need for safe and effective post-infection treatment medication. In this study, we explored a pipeline of 21 potential candidates, examined in the Calu-3 cell line for their antiviral efficacy, for drug repurposing. Ralimetinib and nafamostat, clinically used drugs, have emerged as attractive candidates. Due to the inherent limitations of the selected drugs, we formulated targeted liposomes suitable for both systemic and intranasal administration. Non-targeted and targeted nafamostat liposomes (LipNaf) decorated with an Apolipoprotein B peptide (ApoB-P) as a specific lung-targeting ligand were successfully developed. The developed liposomal formulations of nafamostat were found to possess favorable physicochemical properties including nano size (119-147 nm), long-term stability of the normally rapidly degrading compound in aqueous solution, negligible leakage from the liposomes upon storage, and a neutral surface charge with low polydispersity index (PDI). Both nafamostat and ralimetinib liposomes showed good cellular uptake and lack of cytotoxicity, and non-targeted LipNaf demonstrated enhanced accumulation in the lungs following intranasal (IN) administration in non-infected mice. LipNaf retained its anti-SARS-CoV 2 activity in Calu 3 cells with only a modest decrease, exhibiting complete inhibition at concentrations >100 nM. IN, but not intraperitoneal (IP) treatment with targeted LipNaf resulted in a trend to reduced viral load in the lungs of K18-hACE2 mice compared to targeted empty Lip. Nevertheless, upon removal of outlier data, a statistically significant 1.9-fold reduction in viral load was achieved. This observation further highlights the importance of a targeted delivery into the respiratory tract. In summary, we were able to demonstrate a proof-of-concept of drug repurposing by liposomal formulations with anti-SARS-CoV-2 activity. The biodistribution and bioactivity studies with LipNaf suggest an IN or inhalation route of administration for optimal therapeutic efficacy.

ADAMTS-7 Modulates Atherosclerotic Plaque Formation by Degradation of TIMP-1

BACKGROUND

The ADAMTS7 locus was genome-wide significantly associated with coronary artery disease. Lack of the ECM (extracellular matrix) protease ADAMTS-7 (A disintegrin and metalloproteinase-7) was shown to reduce atherosclerotic plaque formation. Here, we sought to identify molecular mechanisms and downstream targets of ADAMTS-7 mediating the risk of atherosclerosis.

METHODS

Targets of ADAMTS-7 were identified by high-resolution mass spectrometry of atherosclerotic plaques from Apoe-/- and Apoe-/-Adamts7-/- mice. ECM proteins were identified using solubility profiling. Putative targets were validated using immunofluorescence, in vitro degradation assays, coimmunoprecipitation, and Förster resonance energy transfer-based protein-protein interaction assays. ADAMTS7 expression was measured in fibrous caps of human carotid artery plaques.

RESULTS

In humans, ADAMTS7 expression was higher in caps of unstable as compared to stable carotid plaques. Compared to Apoe-/- mice, atherosclerotic aortas of Apoe-/- mice lacking Adamts-7 (Apoe-/-Adamts7-/-) contained higher protein levels of Timp-1 (tissue inhibitor of metalloprotease-1). In coimmunoprecipitation experiments, the catalytic domain of ADAMTS-7 bound to TIMP-1, which was degraded in the presence of ADAMTS-7 in vitro. ADAMTS-7 reduced the inhibitory capacity of TIMP-1 at its canonical target MMP-9 (matrix metalloprotease-9). As a downstream mechanism, we investigated collagen content in plaques of Apoe-/- and Apoe-/-Adamts7-/- mice after a Western diet. Picrosirius red staining of the aortic root revealed less collagen as a readout of higher MMP-9 activity in Apoe-/- as compared to Apoe-/- Adamts7-/- mice. To facilitate high-throughput screening for ADAMTS-7 inhibitors with the aim of decreasing TIMP-1 degradation, we designed a Förster resonance energy transfer-based assay targeting the ADAMTS-7 catalytic site.

CONCLUSIONS

ADAMTS-7, which is induced in unstable atherosclerotic plaques, decreases TIMP-1 stability reducing its inhibitory effect on MMP-9, which is known to promote collagen degradation and is likewise associated with coronary artery disease. Disrupting the interaction of ADAMTS-7 and TIMP-1 might be a strategy to increase collagen content and plaque stability for the reduction of atherosclerosis-related events.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders

E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries such as Asinex for the rational design of E3 ligase binders. The scripts and data associated with this study can be found on GitHub at https://github.com/Fraunhofer-ITMP/E3_binder_Model.

A machine learning method for the identification and characterization of novel COVID-19 drug targets

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

Mpox Knowledge Graph: A comprehensive representation embedding chemical entities and associated biology of Mpox

 

The outbreak of Mpox virus (MPXV) infection in May 2022 is declared a global health emergency by WHO. A total of 84330 cases have been confirmed as of 5th January, 2023 and the numbers are on the rise. The MPXV pathophysiology and its underlying mechanisms are unfortunately not yet understood. Likewise, the knowledge of biochemicals and drugs used against MPXV and their downstream effects is sparse. In this work, using Knowledge Graph (KG) representations we have depicted chemical and biological aspects of MPXV. To achieve this, we have collected and rationally assembled several biological study results, assays, drug candidates, and pre-clinical evidence to form a dynamic and comprehensive network. The KG is compliant with FAIR annotations allowing seamless transformation and integration to/with other formats and infrastructures.

Availability and implementation

The programmatic scripts for Mpox KG are publicly available at https://github.com/Fraunhofer-ITMP/mpox-kg. It is hosted publicly at https://doi.org/10.18119/N9SG7D.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

PEMT: a patent enrichment tool for drug discovery

MOTIVATION

Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources.

RESULTS

Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes.

AVAILABILITY AND IMPLEMENTATION

PEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/, respectively.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Easy access to α-ketoamides as SARS-CoV-2 and MERS Mpro inhibitors via the PADAM oxidation route

SARS-CoV-2 caused worldwide the current outbreak called COVID-19. Despite multiple countermeasures implemented, there is an urgent global need for new potent and efficient antiviral drugs against this pathogen. In this context, the main protease (M^pro) of SARS-CoV-2 is an essential viral enzyme and plays a pivotal role in viral replication and transcription. Its specific cleavage of polypeptides after a glutamine residue has been considered as a key element to design novel antiviral drugs. Herein, we reported the design, synthesis and structure-activity relationships of novel α-ketoamides as covalent reversible inhibitors of M^pro, exploiting the PADAM oxidation route. The reported compounds showed μM to nM activities in enzymatic and in the antiviral cell-based assays against SARS-CoV-2 M^pro.

In order to assess inhibitors’ binding mode, two co-crystal structures of SARS-CoV-2 M^pro in complex with our inhibitors were solved, which confirmed the covalent binding of the keto amide moiety to the catalytic Cys145 residue of M^pro. Finally, in order to interrogate potential broad-spectrum properties, we assessed a selection of compounds against MERS M^pro where they showed nM inhibitory potency, thus highlighting their potential as broad-spectrum coronavirus inhibitors.

Corrigendum to "Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases"[Artificial Intelligence in Life Sciences] 1(2021), 100020

[This corrects the article DOI: 10.1016/j.ailsci.2021.100020.].

Comprehensive Fragment Screening of the SARS‐CoV‐2 Proteome Explores Novel Chemical Space for Drug Development

SARS‐CoV‐2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti‐virals. Within the international Covid19‐NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80% of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR‐detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure‐based drug design against the SCoV2 proteome.

Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort

Worldwide, there are intensive efforts to identify repurposed drugs as potential therapies against SARS-CoV-2 infection and the associated COVID-19 disease. To date, the anti-inflammatory drug dexamethasone and (to a lesser extent) the RNA-polymerase inhibitor remdesivir have been shown to be effective in reducing mortality and patient time to recovery, respectively, in patients. Here, we report the results of a phenotypic screening campaign within an EU-funded project (H2020-EXSCALATE4COV) aimed at extending the repertoire of anti-COVID therapeutics through repurposing of available compounds and highlighting compounds with new mechanisms of action against viral infection. We screened 8702 molecules from different repurposing libraries, to reveal 110 compounds with an anti-cytopathic IC₅₀ < 20 µM. From this group, 18 with a safety index greater than 2 are also marketed drugs, making them suitable for further study as potential therapies against COVID-19. Our result supports the idea that a systematic approach to repurposing is a valid strategy to accelerate the necessary drug discovery process.

Measurement(s)	Cytopathic Effect
Technology Type(s)	confocal fluorescence microscopy
Factor Type(s)	Cellular toxicity
Sample Characteristic - Organism	Chlorocebus sabaeus
Sample Characteristic - Environment	continuant
Sample Characteristic - Location	Belgium

High-throughput drug screening allowed identification of entry inhibitors specifically targeting different routes of SARS-CoV-2 Delta and Omicron/BA.1

The Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2) has continuously evolved, resulting in the emergence of several variants of concern (VOCs). To study mechanisms of viral entry and potentially identify specific inhibitors, we pseudotyped lentiviral vectors with different SARS-CoV-2 VOC spike variants (D614G, Alpha, Beta, Delta, Omicron/BA.1), responsible for receptor binding and membrane fusion. These SARS-CoV-2 lentiviral pseudoviruses were applied to screen 774 FDA-approved drugs. For the assay we decided to use CaCo2 cells, since they equally allow cell entry through both the direct membrane fusion pathway mediated by TMPRSS2 and the endocytosis pathway mediated by cathepsin-L. The active molecules which showed stronger differences in their potency to inhibit certain SARS-CoV-2 VOCs included antagonists of G-protein coupled receptors, like phenothiazine-derived antipsychotic compounds such as Chlorpromazine, with highest activity against the Omicron pseudovirus. In general, our data showed that the various VOCs differ in their preferences for cell entry, and we were able to identify synergistic combinations of inhibitors. Notably, Omicron singled out by relying primarily on the endocytosis pathway while Delta preferred cell entry via membrane fusion. In conclusion, our data provide new insights into different entry preferences of SARS-CoV-2 VOCs, which might help to identify new drug targets.

Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

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Research organisations are focussed on quantifying the costs and benefits of implementing FAIR.

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Criteria used for the selection of data for FAIRification can be opaque and inconsistent.

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FAIRification effort depends on individual skills, competencies, resources, and time available.

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FAIRification should satisfy reuse scenarios, and lead to scientific and economic impacts.

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Organisational challenges include providing training to individuals and developing a FAIR organisation culture.

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

Natural Compounds Inhibit SARS-CoV-2 nsp13 Unwinding and ATPase Enzyme Activities

SARS-CoV-2 infection is still spreading worldwide, and new antiviral therapies are an urgent need to complement the approved vaccine preparations. SARS-CoV-2 nps13 helicase is a validated drug target participating in the viral replication complex and possessing two associated activities: RNA unwinding and 5′-triphosphatase. In the search of SARS-CoV-2 direct antiviral agents, we established biochemical assays for both SARS-CoV-2 nps13-associated enzyme activities and screened both in silico and in vitro a small in-house library of natural compounds. Myricetin, quercetin, kaempferol, and flavanone were found to inhibit the SARS-CoV-2 nps13 unwinding activity at nanomolar concentrations, while licoflavone C was shown to block both SARS-CoV-2 nps13 activities at micromolar concentrations. Mode of action studies showed that all compounds are nsp13 noncompetitive inhibitors versus ATP, while computational studies suggested that they can bind both nucleotide and 5′-RNA nsp13 binding sites, with licoflavone C showing a unique pattern of interaction with nsp13 amino acid residues. Overall, we report for the first time natural flavonoids as selective inhibitors of SARS-CoV-2 nps13 helicase with low micromolar activity.

SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data

One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the “reliability” of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data.

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From summaries of grouped variables, it is possible to generate synthetic data

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Similarity between real and virtual cohorts visualized with Kaplan-Meyer plots

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Shiny app for tuning and downloading the synthetic cohort provided to users

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SASC performances and result accuracies are similar to or better than Synthea results

Virtual cohorts built on synthetic data have received attention lately as they can change the way we work with clinical data. Generating synthetic data very close to "real-world" data without having ethical restrictions can considerably help data analysts in several aspects such as didactic efforts, statistical modeling, and assessments of interventions. We developed SASC, a tool that uses summary statistics of real clinical data to produce synthetic data having the same or a very close summary to the reference variables. SASC displays a performance comparable with, or superior to, that of other virtual cohort tools like Synthea and allows users to optimize the synthetic dataset produced through a Shiny visualizer application interactively.

The blood-brain barrier is dysregulated in COVID-19 and serves as a CNS entry route for SARS-CoV-2

Neurological complications are common in COVID-19. Although SARS-CoV-2 has been detected in patients’ brain tissues, its entry routes and resulting consequences are not well understood. Here, we show a pronounced upregulation of interferon signaling pathways of the neurovascular unit in fatal COVID-19. By investigating the susceptibility of human induced pluripotent stem cell (hiPSC)-derived brain capillary endothelial-like cells (BCECs) to SARS-CoV-2 infection, we found that BCECs were infected and recapitulated transcriptional changes detected in vivo. While BCECs were not compromised in their paracellular tightness, we found SARS-CoV-2 in the basolateral compartment in transwell assays after apical infection, suggesting active replication and transcellular transport of virus across the blood-brain barrier (BBB) in vitro. Moreover, entry of SARS-CoV-2 into BCECs could be reduced by anti-spike-, anti-angiotensin-converting enzyme 2 (ACE2)-, and anti-neuropilin-1 (NRP1)-specific antibodies or the transmembrane protease serine subtype 2 (TMPRSS2) inhibitor nafamostat. Together, our data provide strong support for SARS-CoV-2 brain entry across the BBB resulting in increased interferon signaling.

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IFNγ signaling is upregulated in COVID-19 human neurovascular unit

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SARS-CoV-2-infected hiPS-BCECs display similar upregulation of IFNγ signaling

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SARS-CoV-2 replicates in hiPS-BCECs and is released while barrier remains intact

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SARS-CoV-2 infection of hiPS-BCECs is decreased by antibodies and protease inhibitors

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Structural and Biochemical Analysis of the Dual Inhibition of MG-132 against SARS-CoV-2 Main Protease (Mpro/3CLpro) and Human Cathepsin-L

After almost two years from its first evidence, the COVID-19 pandemic continues to afflict people worldwide, highlighting the need for multiple antiviral strategies. SARS-CoV-2 main protease (Mpro/3CLpro) is a recognized promising target for the development of effective drugs. Because single target inhibition might not be sufficient to block SARS-CoV-2 infection and replication, multi enzymatic-based therapies may provide a better strategy. Here we present a structural and biochemical characterization of the binding mode of MG-132 to both the main protease of SARS-CoV-2, and to the human Cathepsin-L, suggesting thus an interesting scaffold for the development of double-inhibitors. X-ray diffraction data show that MG-132 well fits into the Mpro active site, forming a covalent bond with Cys145 independently from reducing agents and crystallization conditions. Docking of MG-132 into Cathepsin-L well-matches with a covalent binding to the catalytic cysteine. Accordingly, MG-132 inhibits Cathepsin-L with nanomolar potency and reversibly inhibits Mpro with micromolar potency, but with a prolonged residency time. We compared the apo and MG-132-inhibited structures of Mpro solved in different space groups and we identified a new apo structure that features several similarities with the inhibited ones, offering interesting perspectives for future drug design and in silico efforts.

Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Identification of Inhibitors of SARS-CoV-2 3CL-Pro Enzymatic Activity Using a Small Molecule in Vitro Repurposing Screen

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro and have identified 62 additional compounds with IC50 values below 1 μM and profiled their selectivity toward chymotrypsin and 3CL-Pro from the Middle East respiratory syndrome virus. A subset of eight inhibitors showed anticytopathic effect in a Vero-E6 cell line, and the compounds thioguanosine and MG-132 were analyzed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 Å, showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity.

A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Identification of Inhibitors of SARS-CoV-2 3CL-Pro Enzymatic Activity Using a Small Molecule in Vitro Repurposing Screen

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro and have identified 62 additional compounds with IC₅₀ values below 1 μM and profiled their selectivity toward chymotrypsin and 3CL-Pro from the Middle East respiratory syndrome virus. A subset of eight inhibitors showed anticytopathic effect in a Vero-E6 cell line, and the compounds thioguanosine and MG-132 were analyzed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 Å, showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput x-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (Mpro), which is essential for viral replication. In contrast to commonly applied x-ray fragment screening experiments with molecules of low complexity, our screen tested already-approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to Mpro In subsequent cell-based viral reduction assays, one peptidomimetic and six nonpeptidic compounds showed antiviral activity at nontoxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2.

A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied high-content screening to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. The compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro. These compounds were tested in an eight-point dose response screen using the same image-based cytopathicity readout. For the 67 most active molecules, cytotoxicity data were generated to confirm activity against SARS-CoV-2. We verified the ability of known inhibitors camostat, nafamostat, lopinavir, mefloquine, papaverine and cetylpyridinium to reduce the cytopathic effects of SARS-CoV-2, providing confidence in the validity of the assay. The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies.

Discovery of Protein-Protein Interaction Inhibitors by Integrating Protein Engineering and Chemical Screening Platforms

Protein-protein interactions (PPIs) govern intracellular life, and identification of PPI inhibitors is challenging. Roadblocks in assay development stemming from weak binding affinities of natural PPIs impede progress in this field. We postulated that enhancing binding affinity of natural PPIs via protein engineering will aid assay development and hit discovery. This proof-of-principle study targets PPI between linear ubiquitin chains and NEMO UBAN domain, which activates NF-κB signaling. Using phage display, we generated ubiquitin variants that bind to the functional UBAN epitope with high affinity, act as competitive inhibitors, and structurally maintain the existing PPI interface. When utilized in assay development, variants enable generation of robust cell-based assays for chemical screening. Top compounds identified using this approach directly bind to UBAN and dampen NF-κB signaling. This study illustrates advantages of integrating protein engineering and chemical screening in hit identification, a development that we anticipate will have wide application in drug discovery.

Persistent hiccup after surgical resection of a brainstem arteriovenous malformation: a case successfully treated with gabapentin during rehabilitation. Case report

Persistent hiccup rarely occurs during rehabilitation, but its management can prove to be very difficult, particularly in presence of associated dysphagia, requiring longer hospitalization and higher risk of severe clinical complications. We present a case of persistent hiccup after surgical resection of a brainstem arteriovenous malformation successfully treated with gabapentin during rehabilitation.

Novel nanomolar imidazo[4,5-b]pyridines as selective nitric oxide synthase (iNOS) inhibitors: SAR and structural insights

Inducible arginine oxidation and subsequent NO production by correspondent synthase (iNOS) are important cellular answers to proinflammatory signals. Prolonged NO production has been proved in higher organisms to cause stroke or septic shock. Several classes of potent NOS inhibitors have been reported, most of them targeting the arginine binding site of the oxygenase domain. Here we disclose the SAR and the rational design of potent and selective iNOS inhibitors which may be useful as anti-inflammatory drugs.

Synthesis and biological evaluation of novel (4 or 5-aryl)pyrazolyl-indoles as inhibitors of interleukin-2 inducible T-cell kinase (ITK)

Interleukin-2 inducible T-cell kinase (ITK) is one of five kinases that belong to the Tec kinase family that plays an important role in T-cell and mast cell signaling. Various reports point to a role of ITK in the treatment of allergic asthma. For example, it was shown that mice lacking ITK have reduced airway hyperresponsiveness, inflammation and tracheal responses in an allergic asthma model. In this article, we disclose novel ITK inhibitors based on (4 or 5-aryl)pyrazolyl-indole scaffold that were also found to be selective for ITK over other kinases like IRK, CDK2, GSK3ss and PKA.

A Knowledge-Based Weighting Approach to Ligand-Based Virtual Screening

On the basis of the recently introduced reduced graph concept of ErG (extending reduced graphs), a straightforward weighting approach to include additional (e.g., structural or SAR) knowledge into similarity searching procedures for virtual screening (wErG) is proposed. This simple procedure is exemplified with three data sets, for which interaction patterns available from X-ray structures of native or peptidomimetic ligands with their target protein are used to significantly improve retrieval rates of known actives from the MDL Drug Report database. The results are compared to those of other virtual screening techniques such as Daylight fingerprints, FTrees, UNITY, and various FlexX docking protocols. Here, it is shown that wErG exhibits a very good and stable performance independent of the target structure. On the basis of this (and the fact that ErG retrieves structurally more dissimilar compounds due to its potential to perform scaffold-hopping), the combination of wErG and FlexX is successfully explored. Overall, wErG is not only an easily applicable weighting procedure that efficiently identifies actives in large data sets but it is also straightforward to understand for both medicinal and computational chemists and can, therefore, be driven by several aspects of project-related knowledge (e.g., X-ray, NMR, SAR, and site-directed mutagenesis) in a very early stage of the hit identification process.

ErG: 2D Pharmacophore Descriptions for Scaffold Hopping

An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.

A novel glucokinase activator modulates pancreatic islet and hepatocyte function

The glucose-sensing enzyme glucokinase (GK) plays a key role in glucose metabolism. We report here the effects of a novel glucokinase activator, LY2121260. The activator enhanced GK activity via binding to the allosteric site located in the hinge region of the enzyme. LY2121260 stimulated insulin secretion in a glucose-dependent manner in pancreatic beta-cells and increased glucose use in rat hepatocytes. In addition, incubation of beta-cells with the GK activator resulted in increased GK protein levels, suggesting that enhanced insulin secretion on chronic treatment with a GK activator may be due to not only changed enzyme kinetics but also elevated enzyme levels. Animals treated with LY2121260 showed an improved glucose tolerance after oral glucose challenge. These results support the concept that GK activators represent a new class of compounds that increase both insulin secretion and hepatic glucose use and in doing so may prove to be effective agents for the control of blood glucose levels in patients with type 2 diabetes.

Identifying the binding mode of a molecular scaffold

We describe a method for docking of a scaffold-based series and present its advantages over docking of individual ligands, for determining the binding mode of a molecular scaffold in a binding site. The method has been applied to eight different scaffolds of protein kinase inhibitors (PKI). A single analog of each of these eight scaffolds was previously crystallized with different protein kinases. We have used FlexX to dock a set of molecules that share the same scaffold, rather than docking a single molecule. The main mode of binding is determined by the mode of binding of the largest cluster among the docked molecules that share a scaffold. Clustering is based on our 'nearest single neighbor' method [J. Chem. Inf. Comput. Sci., 43 (2003) 208-217]. Additional criteria are applied in those cases in which more than one significant binding mode is found. Using the proposed method, most of the crystallographic binding modes of these scaffolds were reconstructed. Alternative modes, that have not been detected yet by experiments, could also be identified. The method was applied to predict the binding mode of an additional molecular scaffold that was not yet reported and the predicted binding mode has been found to be very similar to experimental results for a closely related scaffold. We suggest that this approach be used as a virtual screening tool for scaffold-based design processes.

The antitumor histone deacetylase inhibitor suberoylanilide hydroxamic acid exhibits antiinflammatory properties via suppression of cytokines

Suberoylanilide hydroxamic acid (SAHA) is a hydroxamic acid-containing hybrid polar molecule; SAHA specifically binds to and inhibits the activity of histone deacetylase. Although SAHA, like other inhibitors of histone deacetylase, exhibits antitumor effects by increasing expression of genes regulating tumor survival, we found that SAHA reduces the production of proinflammatory cytokines in vivo and in vitro. A single oral administration of SAHA to mice dose-dependently reduced circulating TNF-alpha, IL-1-beta, IL-6, and IFN-gamma induced by lipopolysaccharide (LPS). Administration of SAHA also reduced hepatic cellular injury in mice following i.v. injection of Con A. SAHA inhibited nitric oxide release in mouse macrophages stimulated by the combination of TNF-alpha plus IFN-gamma. Human peripheral blood mononuclear cells stimulated with LPS in the presence of SAHA released less TNF-alpha, IL-1-beta, IL-12, and IFN-gamma (50% reduction at 100-200 nM). The production of IFN-gamma stimulated by IL-18 plus IL-12 was also inhibited by SAHA (85% at 200 nM). However, SAHA did not affect LPS-induced synthesis of the IL-1-beta precursor, the IL-1 receptor antagonist, or the chemokine IL-8. In addition, IFN-gamma induced by anti-CD3 was not suppressed by SAHA. Steady-state mRNA levels for LPS-induced TNF-alpha and IFN-gamma in peripheral blood mononuclear cells were markedly decreased, whereas IL-8 and IL-1-beta mRNA levels were unaffected. Because SAHA exhibits antiinflammatory properties in vivo and in vitro, inhibitors of histone deacetylase may stimulate the expression of genes that control the synthesis of cytokines and nitric oxide or hyperacetylate other targets.

Solvation enthalpies as descriptors of structure--in vitro percutaneous permeation relationship of benzoxazinones regioisomers

The aim of this work was to correlate the in vitro human skin permeability, expressed as the permeability coefficient (Kp), and some physicochemical parameters of a new series of benzoxazinones. The in vitro human skin permeability of 14 substances, including regioisomers with CH3, OH, OCH3, and Cl groups in different positions on the aromatic ring, was determined. The modified Franz diffusion cell method was used. The Kp values were in the range 0.14-8.24 cm/h, showing a strong dependence on the position and type of substituent. Physicochemical descriptors usually referred in literature, such as log P, molecular weight and volume (MV), hydrogen bond donor (Hd) and acceptor activity (Ha), and molecular refractivity were considered, with the addition of solvation enthalpy (delta deltaHsolv). Delta deltaHsolv is defined as the difference between formation enthalpies in water and octanol. The algorithm with the best correlation between Kp and physicochemical descriptors was calculated, taking into account the differences observed among the regioisomers. The algorithm obtained with delta deltaHsolv had a good correlation (r2 = 0.749, F = 16.43, P = 0.0005), comparable with the equation, proposed by Potts and Guy, based on MV, Hd and Ha (r2 = 0.830, F = 16.3, P = 0.0004).

Global 3D-QSAR methods: MS-WHIM and autocorrelation

The recently proposed MS-WHIM indices, a set of theoretical descriptors containing information about size, shape and electrostatic distribution of a molecule, have been further investigated. The main objectives of this work were: (i) to confirm the descriptive power of MS-WHIM in modelling specific biological interactions, (ii) to analyse the dependence of MS-WHIM on the type of atomic charges used for computing electrostatic potential and (iii) to compare the performances of MS-WHIM with those provided by other global 3D molecular descriptors. The spatial autocorrelation of atomic and molecular surface properties were selected for comparison purposes. WHIM-based and autocorrelation-based vectors were calculated for two molecular sets from the literature, namely a series of 18 HIV-1 reverse transcriptase inhibitors and a set of 36 sulphonamide endothelin inhibitors. PLS was adopted to derive statistical predictive models that were validated by means of cross-validation. The reported results confirmed that MS-WHIM indices are able to provide meaningful statistical correlations with biological activity. MS-WHIM descriptors are sensitive to the type of partial atomic charges applied and improved models were obtained using more accurate charges. Moreover for both the datasets, MS-WHIM results, in terms of fitting and predictive power of PLS models, were superior to those from autocorrelation. Finally, the strengths/weaknesses of global 3D-QSAR descriptors over local CoMFA-like methods, as well as the main differences between WHIM-based and autocorrelation-based vectors, are discussed.

The interaction of myristylated peptides with the catalytic domain of protein kinase C revealed by their sequence palindromy and the identification of a myristyl binding site

Using a model of the enzyme structure and the results from a series of free and myristylated peptides, we provide evidence that peptides corresponding to the pseudosubstrate sequence of protein kinase C bind to the enzyme substrate binding site in an essentially extended conformation. This and the nearly symmetrical location of positive charges around the substrate phosphoritable site allow the peptide to bind to the enzyme in either an N-to-C orientation or its C-to-N opposite orientation. The latter is favoured by a change in residue chirality or when the peptide bears a myristoyl chain at its N-terminus. A myristyl binding site was also identified in the enzyme structure and its location in a region proximal to the C-terminal residue of pseudosubstrate bound in the N-to-C direction suggested that C-myristylation of peptide substrates should be more effective than N-myristoylation in antagonizing the enzyme. A peptide (H-RFARKGALRQKN-CONH-Myr) which contains the myristyl chain covalently linked to the C-terminal residue of the pseudosubstrate was thus made and shown to be a potent inhibitor of the histone kinase reaction of protein kinase C and the phosphorylation of p47 in intact cells.