Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp15 endoribonuclease
Berta CanalRyo FujisawaAllison W. McClureTom DeeganMary WuRachel UlfertsFlorian WeissmannLucy S. DruryAgustina P. BertolinJingkun ZengRupert BealeMichael HowellKarim LabibJohn F.X. Diffley
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SARS-CoV-2 is responsible for COVID-19, a human disease that has caused over 2 million deaths, stretched health systems to near-breaking point and endangered economies of countries and families around the world. Antiviral treatments to combat COVID-19 are currently lacking. Remdesivir, the only antiviral drug approved for the treatment of COVID-19, can affect disease severity, but better treatments are needed. SARS-CoV-2 encodes 16 non-structural proteins (nsp) that possess different enzymatic activities with important roles in viral genome replication, transcription and host immune evasion. One key aspect of host immune evasion is performed by the uridine-directed endoribonuclease activity of nsp15. Here we describe the expression and purification of nsp15 recombinant protein. We have developed biochemical assays to follow its activity, and we have found evidence for allosteric behaviour. We screened a custom chemical library of over 5000 compounds to identify nsp15 endoribonuclease inhibitors, and we identified and validated NSC95397 as an inhibitor of nsp15 endoribonuclease in vitro. Although NSC95397 did not inhibit SARS-CoV-2 growth in VERO E6 cells, further studies will be required to determine the effect of nsp15 inhibition on host immune evasion.Keywords:
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Abstract: Protein–protein interactions (PPIs) are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in "hot spots", has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of "standard" fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base) functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three-dimensional character. These physiochemical properties can potentially be exploited in the rational design of PPI-specific fragment libraries and correlate well with optimized PPI inhibitors, which tend to have properties outside currently accepted guidelines for drug-likeness. Several examples of small-molecule PPI inhibitors derived from fragment-based drug discovery now exist and are described in this review, including navitoclax, a novel Bcl-2 family inhibitor which has entered Phase II clinical trials in patients with small-cell lung cancer and chronic lymphocytic leukemia. Keywords: hot spot, druggability, ligandability
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This chapter contains sections titled: Introduction Druggability: Ligand Properties Druggability: Ligand Binding Druggability Prediction by Protein Class Druggability Predictions: Experimental Methods Druggability Predictions: Computational Methods A Test Case: PTP1B Outlook and Concluding Remarks References
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Protein tyrosine phosphatases (PTP) play important roles in the pathogenesis of many diseases. The fact that no PTP inhibitors have reached the market so far has raised many questions about their druggability. In this study, the active sites of 17 PTPs were characterized and assessed for its ability to bind drug-like molecules. Consequently, PTPs were classified according to their druggability scores into four main categories. Only four members showed intermediate to very druggable pocket; interestingly, the rest of them exhibited poor druggability. Particularly focusing on PTP1B, we also demonstrated the influence of several factors on the druggability of PTP active site. For instance, the open conformation showed better druggability than the closed conformation, while the tight-bound water molecules appeared to have minimal effect on the PTP1B druggability. Finally, the allosteric site of PTP1B was found to exhibit superior druggability compared to the catalytic pocket. This analysis can prove useful in the discovery of new PTP inhibitors by assisting researchers in predicting hit rates from high throughput or virtual screening and saving unnecessary cost, time, and efforts via prioritizing PTP targets according to their predicted druggability.
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Abstract Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology.
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Abstract The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.
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Drugs that function as enzyme inhibitors constitute a significant portion of the orally bioavailable therapeutic agents that are in clinical use today. Likewise, much of drug discovery and development efforts at present are focused on identifying and optimizing drug candidates that act through inhibition of specific enzyme targets. The attractiveness of enzymes as targets for drug discovery stems from the high levels of disease association (target validation) and druggability (target tractability) that typically characterize this class of proteins. In this expert opinion the authors describe the existing practices and future directions in drug discovery enzymology, with emphasis on how a detailed understanding of the catalytic mechanism of specific targets can be used to identify and optimize small-molecule compounds that interact with conformationally distinct forms of the enzyme, thus resulting in high potency, high selectivity inhibitors.
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