Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

A Database for Large-Scale Docking and Experimental Results

The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully shared, hindering the benchmarking of machine learning and chemical space exploration methods that seek to explore the expanding chemical spaces. To address this gap, we develop a website providing access to recent large library campaigns, including poses, scores, and in vitro results for campaigns against 11 targets, with 6.3 billion molecules docked and 3729 compounds experimentally tested. In a simple proof-of-concept study that speaks to the new library's utility, we use the new database to train machine learning models to predict docking scores and to find the top 0.01% scoring molecules while evaluating only 1% of the library. Even in these proof-of-concept studies, some interesting trends emerge: unsurprisingly, as models train on larger sets, they perform better; less expectedly, models could achieve high correlations with docking scores and yet still fail to enrich the new docking-discovered ligands, or even the top 0.01% of docking-ranked molecules. It will be interesting to see how these trends develop for methods more sophisticated than the simple proof-of-concept studies undertaken here; the database is openly available at lsd.docking.org.

Emergence of specific binding and catalysis from a designed generalist binding protein

The evolution of binding and catalysis played a central role in the emergence of life. While natural proteins have finely tuned affinities for their primary ligands, they also bind weakly and promiscuously to other molecules, which serve as starting points for stepwise, incremental evolution of entirely new specificities. Thus, modern proteins emerged from the joint exploration of sequence and structural space. The ability of natural proteins to bind promiscuously to small molecule fragments has been widely evaluated using methods including crystallographic fragment screening. However, this approach had not been applied to de novo proteins. Here, we apply this method to explore the promiscuity of a de novo small molecule-binding protein ABLE. As in Nature, we found ABLE was capable of forming weak complexes, which were found to be excellent starting points for evolving entirely new functions, including a binder of a turn-on fluorophore and a highly efficient and specific Kemp eliminase enzyme. This work shows how Nature and protein designers can take advantage of promiscuous binding interactions to evolve new proteins with specialized functions.

Interferon-induced ADP-ribosylation: technical developments driving ICAB discovery

Cells respond to a variety of internal and external stimuli by regulating the activities of different signalling cascades and cellular processes, often via chemical modifications of biological macromolecules that modulate their overall levels, biochemical activities or biophysical interactions. One such modification, termed ADP-ribosylation (ADPr), is emerging as an important player in the interferon (IFN) response, but the molecular targets and functions of ADP-ribosyltransferases within this core component of innate immunity still remains unclear. We and others have recently identified that stimulation of IFN signalling cascades promotes the formation of a novel cytosolic structure in human cells that is enriched in ADP-ribosyl modifications. Here, we propose to name these structures 'interferon-induced cytosolic ADPr bodies' (ICABs) and discuss their known components and potential functions. We also review methods to detect ICABs (and cellular ADPr in general) using a range of recently developed reagents. This lays the foundation for future studies aimed at elucidating the molecular functions of ICABs and ADPr in innate immune responses, which is a central unanswered question in the field.

A database for large-scale docking and experimental results

The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully shared, hindering the benchmarking of machine learning and chemical space exploration methods that seek to explore the expanding chemical spaces. To address this gap, we develop a website providing access to recent large library campaigns, including poses, scores, and in vitro results for campaigns against 11 targets, with 6.3 billion molecules docked and 3729 compounds experimentally tested. In a simple proof-of-concept study that speaks to the new library's utility, we use the new database to train machine learning models to predict docking scores and to find the top 0.01% scoring molecules while evaluating only 1% of the library. Even in these proof-of-concept studies, some interesting trends emerge: unsurprisingly, as models train on larger sets, they perform better; less expected, models could achieve high correlations with docking scores and yet still fail to enrich the new docking-discovered ligands, or even the top 0.01% of docking-ranked molecules. It will be interesting to see how these trends develop for methods more sophisticated than the simple proof-of-concept studies undertaken here; the database is openly available at lsd.docking.org.

Virtual fragment screening for DNA repair inhibitors in vast chemical space

Fragment-based screening can catalyze drug discovery by identifying novel scaffolds, but this approach is limited by the small chemical libraries studied by biophysical experiments and the challenging optimization process. To expand the explored chemical space, we employ structure-based docking to evaluate orders-of-magnitude larger libraries than those used in traditional fragment screening. We computationally dock a set of 14 million fragments to 8-oxoguanine DNA glycosylase (OGG1), a difficult drug target involved in cancer and inflammation, and evaluate 29 highly ranked compounds experimentally. Four of these bind to OGG1 and X-ray crystallography confirms the binding modes predicted by docking. Furthermore, we show how fragment elaboration using searches among billions of readily synthesizable compounds identifies submicromolar inhibitors with anti-inflammatory and anti-cancer effects in cells. Comparisons of virtual screening strategies to explore a chemical space of 1022 compounds illustrate that fragment-based design enables enumeration of all molecules relevant for inhibitor discovery. Virtual fragment screening is hence a highly efficient strategy for navigating the rapidly growing combinatorial libraries and can serve as a powerful tool to accelerate drug discovery efforts for challenging therapeutic targets.

Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease

FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated de novo design. We interface the workflow with active learning to improve the efficiency of searching the combinatorial space of possible linkers and functional groups, make use of interactions formed by crystallographic fragments in scoring compound designs, and introduce the option to seed the chemical space with molecules available from on-demand chemical libraries. As a test case, we target the main protease (Mpro) of SARS-CoV-2, identifying several small molecules with high similarity to molecules discovered by the COVID moonshot effort, using only structural information from a fragment screen in a fully automated fashion. Finally, we order and test 19 compound designs, of which three show weak activity in a fluorescence-based Mpro assay, but work is needed to further optimise the prioritisation of compounds for purchase. The FEgrow package and full tutorials demonstrating the active learning workflow are available at https://github.com/cole-group/FEgrow.

Fragment-to-Lead Medicinal Chemistry Publications in 2023

This Perspective summarizes successful fragment-to-lead (F2L) studies that were published in 2023 and is the ninth installment in an annual series. A tabulated summary of the relevant articles published in 2023 is provided (17 entries from 16 articles), and a comparison of the target classes, screening methods, and overall fragment or lead property trends for 2023 examples and for the combined entries over the years 2015-2023 is discussed. In addition, we identify several trends and innovations in the 2023 literature that promise to further increase the success of fragment-based drug discovery (FBDD), particularly in the areas of NMR and virtual screening, fragment library design, and fragment linking.

Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding-based methodology

Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein .Scientific contributionThis work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.

Exploring pyrazolines as potential inhibitors of NSP3-macrodomain of SARS-CoV-2: synthesis and in silico analysis

COVID-19 has proved to be a global health crisis during the pandemic, and the emerging JN.1 variant is a potential threat. Therefore, finding alternative antivirals is of utmost priority. In the current report, we present the synthesis of new and potential anti-viral pyrazoline compounds. Here we report a chemical scheme where β-aryl β-anilino ketones react with phenyl hydrazine in potassium hydroxide to give the corresponding 3,5-diarylpyrazoline. The protocol is applicable to a variety of β-amino ketones and tolerates several functional groups. This method is efficient and proceeds regioselectivity since the β-Anilino group acts as a protecting group for alkenes of chalcones. We identified the NSP3-microdomain (Mac-1) of SARS-CoV-2 as a putative target for newly synthesized triaryl-2-pyrazoline compounds. The molecular dynamics simulation-based free energy estimation suggests compounds 7a, 7d, 7 g, 7i, 7k, and 7 L as promising Mac-1 inhibitors. The detailed structural inspection of MD simulation trajectories sheds light on the structural and functional dynamics involved in the SARS-CoV-2 Mac-1. The data presented here is expected to guide the design and development of better anti-SARS-CoV-2 therapies.

Protein-adaptive differential scanning fluorimetry using conformationally responsive dyes

Differential scanning fluorimetry (DSF) is a technique that reports protein thermal stability via the selective recognition of unfolded states by fluorogenic dyes. However, DSF applications remain limited by protein incompatibilities with existing DSF dyes. Here we overcome this obstacle with the development of a protein-adaptive DSF platform (paDSF) that combines a dye library 'Aurora' with a streamlined procedure to identify protein-dye pairs on demand. paDSF was successfully applied to 94% (66 of 70) of proteins, tripling the previous compatibility and delivering assays for 66 functionally and biochemically diverse proteins, including 10 from severe acute respiratory syndrome coronavirus 2. We find that paDSF can be used to monitor biological processes that were previously inaccessible, demonstrated for the interdomain allostery of O-GlcNAc transferase. The chemical diversity and varied selectivities of Aurora dyes suggest that paDSF functionality may be readily extended. paDSF is a generalizable tool to interrogate protein stability, dynamics and ligand binding.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Structure-based drug design with equivariant diffusion models

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new drug candidates. However, most existing methods focus exclusively on bottom-up de novo design of compounds or tackle other drug development challenges with task-specific models. The latter requires curation of suitable datasets, careful engineering of the models and retraining from scratch for each task. Here we show how a single pretrained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design and partial molecular design with inpainting. We formulate SBDD as a three-dimensional conditional generation problem and present DiffSBDD, an SE(3)-equivariant diffusion model that generates novel ligands conditioned on protein pockets. Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics.

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening

The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compounds nominated in a virtual screen not engaging the intended target protein to any detectable extent. Emerging machine learning approaches have made significant progress in this regard, including our previously described tool vScreenML. The broad adoption of vScreenML was hindered by its challenging usability and dependencies on certain obsolete or proprietary software packages. Here, we introduce vScreenML 2.0 to address each of these limitations with a streamlined Python implementation. Through careful benchmarks, we show that vScreenML 2.0 outperforms other widely used tools for virtual screening hit discovery.

Identification of a series of pyrrolo-pyrimidine based SARS-CoV-2 Mac1 inhibitors that repress coronavirus replication

Coronaviruses (CoVs) can emerge from zoonotic sources and cause severe diseases in humans and animals. All CoVs encode for a macrodomain (Mac1) that binds to and removes ADP-ribose from target proteins. SARS-CoV-2 Mac1 promotes virus replication in the presence of interferon (IFN) and blocks the production of IFN, though the mechanisms by which it mediates these functions remain unknown. Mac1 inhibitors could help elucidate these mechanisms and serve as therapeutic agents against CoV-induced diseases. We previously identified compound 4a (a.k.a. MCD-628), a pyrrolo-pyrimidine that inhibited Mac1 activity in vitro at low micromolar levels. Here, we determined the binding mode of 4a by crystallography, further defining its interaction with Mac1. However, 4a did not reduce CoV replication, which we hypothesized was due to its acidic side chain limiting permeability. To test this hypothesis, we developed several hydrophobic derivatives of 4a . We identified four compounds that both inhibited Mac1 in vitro and inhibited murine hepatitis virus (MHV) replication: 5a , 5c , 6d , and 6e . Furthermore, 5c and 6e inhibited SARS-CoV-2 replication only in the presence of IFN γ , similar to a Mac1 deletion virus. To confirm their specificity, we passaged MHV in the presence of 5a to identify drug-resistant mutations and identified an alanine-to-threonine and glycine-to-valine double mutation in Mac1. Recombinant virus with these mutations had enhanced replication compared to WT virus when treated with 5a , demonstrating the specificity of these compounds during infection. However, this virus is highly attenuated in vivo , indicating that drug-resistance emerged at the expense of viral fitness.

IMPORTANCE

Coronaviruses (CoVs) present significant threats to human and animal health, as evidenced by recent outbreaks of MERS-CoV and SARS-CoV-2. All CoVs encode for a highly conserved macrodomain protein (Mac1) that binds to and removes ADP-ribose from proteins, which promotes virus replication and blocks IFN production, though the exact mechanisms remain unclear. Inhibiting Mac1 could provide valuable insights into these mechanisms and offer new therapeutic avenues for CoV-induced diseases. We have identified several unique pyrrolo-pyrimidine-based compounds as Mac1 inhibitors. Notably, at least two of these compounds inhibited both murine hepatitis virus (MHV) and SARS-CoV-2 replication. Furthermore, we identified a drug-resistant mutation in Mac1, confirming target specificity during infection. However, this mutant is highly attenuated in mice, indicating that drug-resistance appears to come at a fitness cost. These results emphasize the potential of Mac1 as a drug target and the promise of structure-based inhibitor design in combating coronavirus infections.

Identifying Artifacts from Large Library Docking

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown the hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation as cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against hits from docking against AmpC β-lactamase. We prioritized 128 high-ranking molecules for synthesis and testing, a mixture of 39 molecules flagged as likely cheaters and 89 that were plausible inhibitors. None of the predicted cheating compounds inhibited AmpC detectably, while 57% of the 89 plausible compounds did so. As our libraries continue to grow, deprioritizing docking artifacts by rescoring with orthogonal methods may find wide use.

Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments

Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is generally reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including the use of routines within RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our previous version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents.

The Mac1 ADP-ribosylhydrolase is a Therapeutic Target for SARS-CoV-2

SARS-CoV-2 continues to pose a threat to public health. Current therapeutics remain limited to direct acting antivirals that lack distinct mechanisms of action and are already showing signs of viral resistance. The virus encodes an ADP-ribosylhydrolase macrodomain (Mac1) that plays an important role in the coronaviral lifecycle by suppressing host innate immune responses. Genetic inactivation of Mac1 abrogates viral replication in vivo by potentiating host innate immune responses. However, it is unknown whether this can be achieved by pharmacologic inhibition and can therefore be exploited therapeutically. Here we report a potent and selective lead small molecule, AVI-4206, that is effective in an in vivo model of SARS-CoV-2 infection. Cellular models indicate that AVI-4206 has high target engagement and can weakly inhibit viral replication in a gamma interferon- and Mac1 catalytic activity-dependent manner; a stronger antiviral effect for AVI-4206 is observed in human airway organoids. In an animal model of severe SARS-CoV-2 infection, AVI-4206 reduces viral replication, potentiates innate immune responses, and leads to a survival benefit. Our results provide pharmacological proof of concept that Mac1 is a valid therapeutic target via a novel immune-restoring mechanism that could potentially synergize with existing therapies targeting distinct, essential aspects of the coronaviral life cycle. This approach could be more widely used to target other viral macrodomains to develop antiviral therapeutics beyond COVID-19.

Extensive exploration of structure activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment merging and active learning

The macrodomain contained in the SARS-CoV-2 non-structural protein 3 (NSP3) is required for viral pathogenesis and lethality. Inhibitors that block the macrodomain could be a new therapeutic strategy for viral suppression. We previously performed a large-scale X-ray crystallography-based fragment screen and discovered a sub-micromolar inhibitor by fragment linking. However, this carboxylic acid-containing lead had poor membrane permeability and other liabilities that made optimization difficult. Here, we developed a shape-based virtual screening pipeline - FrankenROCS - to identify new macrodomain inhibitors using fragment X-ray crystal structures. We used FrankenROCS to exhaustively screen the Enamine high-throughput screening (HTS) collection of 2.1 million compounds and selected 39 compounds for testing, with the most potent compound having an IC50 value equal to 130 μM. We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent having an IC50 equal to 220 μM. Further optimization led to analogs with IC50 values better than 10 μM, with X-ray crystal structures revealing diverse binding modes despite conserved chemical features. These analogs represent a new lead series with improved membrane permeability that is poised for optimization. In addition, the collection of 137 X-ray crystal structures with associated binding data will serve as a resource for the development of structure-based drug discovery methods. FrankenROCS may be a scalable method for fragment linking to exploit ever-growing synthesis-on-demand libraries.

Identifying novel chemical matter against the Chikungunya virus nsP3 macrodomain through crystallographic fragment screening

Chikungunya virus (CHIKV) causes severe fever, rash and debilitating joint pain that can last for months1,2or even years. Millions of people have been infected with CHIKV, mostly in low and middle-income countries, and the virus continues to spread into new areas due to the geographical expansion of its mosquito hosts. Its genome encodes a macrodomain, which functions as an ADP-ribosyl hydrolase, removing ADPr from viral and host-cell proteins interfering with the innate immune response. Mutational studies have shown that the CHIKV nsP3 macrodomain is necessary for viral replication, making it a potential target for the development of antiviral therapeutics. We, therefore, performed a high-throughput crystallographic fragment screen against the CHIKV nsP3 macrodomain, yielding 109 fragment hits covering the ADPr-binding site and two adjacent subsites that are absent in the homologous macrodomain of SARS-CoV-2 but may be present in other alphaviruses, such as Venezuelan equine encephalitis virus (VEEV) and eastern equine encephalitis virus (EEEV). Finally, a subset of overlapping fragments was used to manually design three fragment merges covering the adenine and oxyanion subsites. The rich dataset of chemical matter and structural information discovered from this fragment screen is publicly available and can be used as a starting point for developing a CHIKV nsP3 macrodomain inhibitor.

Discovery of potent SARS-CoV-2 nsp3 macrodomain inhibitors uncovers lack of translation to cellular antiviral response

A strategy for pandemic preparedness is the development of antivirals against a wide set of viral targets with complementary mechanisms of action. SARS-CoV-2 nsp3-mac1 is a viral macrodomain with ADP-ribosylhydrolase activity, which counteracts host immune response. Targeting the virus' immunomodulatory functionality offers a differentiated strategy to inhibit SARS-CoV-2 compared to approved therapeutics, which target viral replication directly. Here we report a fragment-based lead generation campaign guided by computational approaches. We discover tool compounds which inhibit nsp3-mac1 activity at low nanomolar concentrations, and with responsive structure-activity relationships, high selectivity, and drug-like properties. Using our inhibitors, we show that inhibition of nsp3-mac1 increases ADP-ribosylation, but surprisingly does not translate to demonstrable antiviral activity in cell culture and iPSC-derived pneumocyte models. Further, no synergistic activity is observed in combination with interferon gamma, a main protease inhibitor, nor a papain-like protease inhibitor. Our results question the extent to which targeting modulation of innate immunity-driven ADP-ribosylation can influence SARS-CoV-2 replication. Moreover, these findings suggest that nsp3-mac1 might not be a suitable target for antiviral therapeutics development.

An expanded trove of fragment-bound structures for the allosteric enzyme PTP1B from computational reanalysis of large-scale crystallographic data

Due to their low binding affinities, detecting small-molecule fragments bound to protein structures from crystallographic datasets has been a challenge. Here, we report a trove of 65 new fragment hits for PTP1B, an "undruggable" therapeutic target enzyme for diabetes and cancer. These structures were obtained from computational analysis of data from a large crystallographic screen, demonstrating the power of this approach to elucidate many (∼50% more) "hidden" ligand-bound states of proteins. Our new structures include a fragment hit found in a novel binding site in PTP1B with a unique location relative to the active site, one that links adjacent allosteric sites, and, perhaps most strikingly, a fragment that induces long-range allosteric protein conformational responses. Altogether, our research highlights the utility of computational analysis of crystallographic data, makes publicly available dozens of new ligand-bound structures of a high-value drug target, and identifies novel aspects of ligandability and allostery in PTP1B.

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

HTRF-based assay for detection of mono-ADP-ribosyl hydrolyzing macrodomains and inhibitor screening

The COVID-19 pandemic has highlighted the lack of effective, ready-to-use antivirals for the treatment of viruses with pandemic potential. The development of a diverse drug portfolio is therefore crucial for pandemic preparedness. Viral macrodomains are attractive therapeutic targets as they are suggested to play an important role in evading the innate host immune response, making them critical for viral pathogenesis. Macrodomains function as erasers of mono-ADP-ribosylation (deMARylation), a post-translational modification that is involved in interferon signaling. Herein, we report the development of a modular HTRF-based assay, that can be used to screen for inhibitors of various viral and human macrodomains. We characterized the five most promising small molecule SARS-CoV-2 Mac1 inhibitors recently reported in the literature for potency and selectivity and conducted a pilot screen demonstrating HTS suitability. The ability to directly detect enzymatic activity makes the DeMAR assay a valuable addition to the existing tools for macrodomain drug discovery.

Synthesis of Structural ADP-Ribose Analogues as Inhibitors for SARS-CoV-2 Macrodomain 1

Protein adenosine diphosphate (ADP)-ribosylation is crucial for a proper immune response. Accordingly, viruses have evolved ADP-ribosyl hydrolases to remove these modifications, a prominent example being the SARS-CoV-2 NSP3 macrodomain, "Mac1". Consequently, inhibitors are developed by testing large libraries of small molecule candidates, with considerable success. However, a relatively underexplored angle in design pertains to the synthesis of structural substrate mimics. Here, we present the synthesis and biophysical activity of novel adenosine diphosphate ribose (ADPr) analogues as SARS-CoV-2 NSP3 Mac1 inhibitors.

PARP14 and PARP9/DTX3L regulate interferon-induced ADP-ribosylation

PARP-catalysed ADP-ribosylation (ADPr) is important in regulating various cellular pathways. Until recently, PARP-dependent mono-ADP-ribosylation has been poorly understood due to the lack of sensitive detection methods. Here, we utilised an improved antibody to detect mono-ADP-ribosylation. We visualised endogenous interferon (IFN)-induced ADP-ribosylation and show that PARP14 is a major enzyme responsible for this modification. Fittingly, this signalling is reversed by the macrodomain from SARS-CoV-2 (Mac1), providing a possible mechanism by which Mac1 counteracts the activity of antiviral PARPs. Our data also elucidate a major role of PARP9 and its binding partner, the E3 ubiquitin ligase DTX3L, in regulating PARP14 activity through protein-protein interactions and by the hydrolytic activity of PARP9 macrodomain 1. Finally, we also present the first visualisation of ADPr-dependent ubiquitylation in the IFN response. These approaches should further advance our understanding of IFN-induced ADPr and ubiquitin signalling processes and could shed light on how different pathogens avoid such defence pathways.

Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

PARP14 is regulated by the PARP9/DTX3L complex and promotes interferon γ-induced ADP-ribosylation

Protein ADP-ribosylation plays important but ill-defined roles in antiviral signalling cascades such as the interferon response. Several viruses of clinical interest, including coronaviruses, express hydrolases that reverse ADP-ribosylation catalysed by host enzymes, suggesting an important role for this modification in host-pathogen interactions. However, which ADP-ribosyltransferases mediate host ADP-ribosylation, what proteins and pathways they target and how these modifications affect viral infection and pathogenesis is currently unclear. Here we show that host ADP-ribosyltransferase activity induced by IFNγ signalling depends on PARP14 catalytic activity and that the PARP9/DTX3L complex is required to uphold PARP14 protein levels via post-translational mechanisms. Both the PARP9/DTX3L complex and PARP14 localise to IFNγ-induced cytoplasmic inclusions containing ADP-ribosylated proteins, and both PARP14 itself and DTX3L are likely targets of PARP14 ADP-ribosylation. We provide evidence that these modifications are hydrolysed by the SARS-CoV-2 Nsp3 macrodomain, shedding light on the intricate cross-regulation between IFN-induced ADP-ribosyltransferases and the potential roles of the coronavirus macrodomain in counteracting their activity.

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

DSFworld: A flexible and precise tool to analyze differential scanning fluorimetry data

Differential scanning fluorimetry (DSF) is a method to determine the apparent melting temperature (Tma) of a purified protein. In DSF, the raw unfolding curves from which Tma is calculated vary widely in shape and complexity. However, the tools available for calculating Tma are only compatible with the simplest of DSF curves, hindering many otherwise straightforward applications of the technology. To overcome this limitation, we designed new mathematical models for Tma calculation that accommodate common forms of variation in DSF curves, including the number of transitions, the presence of high initial signal, and temperature-dependent signal decay. When tested these models against DSFbase, an open-source database of 6235 raw, real-life DSF curves, these models outperformed the existing standard approaches of sigmoid fitting and maximum of the first derivative. To make these models accessible, we created an open-source software and website, DSFworld (https://gestwickilab.shinyapps.io/dsfworld/). In addition to these improved fitting capabilities, DSFworld also includes features that overcome the practical limitations of many analysis workflows, including automatic reformatting of raw data exported from common qPCR instruments, labeling of data based on experimental variables, and flexible interactive plotting. We hope that DSFworld will enable more streamlined and accurate calculation of Tma values for DSF experiments.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

GS-441524-Diphosphate-Ribose Derivatives as Nanomolar Binders and Fluorescence Polarization Tracers for SARS-CoV-2 and Other Viral Macrodomains

Viral macrodomains that can bind to or hydrolyze protein adenosine diphosphate ribosylation (ADP-ribosylation) have emerged as promising targets for antiviral drug development. Many inhibitor development efforts have been directed against the severe acute respiratory syndrome coronavirus 2 macrodomain 1 (SARS-CoV-2 Mac1). However, potent inhibitors for viral macrodomains are still lacking, with the best inhibitors still in the micromolar range. Based on GS-441524, a remdesivir precursor, and our previous studies, we have designed and synthesized potent binders of SARS-CoV-2 Mac1 and other viral macrodomains including those of Middle East respiratory syndrome coronavirus (MERS-CoV), Venezuelan equine encephalitis virus (VEEV), and Chikungunya virus (CHIKV). We show that the 1'-CN group of GS-441524 promotes binding to all four viral macrodomains tested while capping the 1″-OH of GS-441524-diphosphate-ribose with a simple phenyl ring further contributes to binding. Incorporating these two structural features, the best binders show 20- to 6000-fold increases in binding affinity over ADP-ribose for SARS-CoV-2, MERS-CoV, VEEV, and CHIKV macrodomains. Moreover, building on these potent binders, we have developed two highly sensitive fluorescence polarization tracers that only require nanomolar proteins and can effectively resolve the binding affinities of nanomolar inhibitors. Our findings and probes described here will facilitate future development of more potent viral macrodomain inhibitors.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Discovery of 2-Amide-3-methylester Thiophenes that Target SARS-CoV-2 Mac1 and Repress Coronavirus Replication, Validating Mac1 as an Antiviral Target

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has made it clear that further development of antiviral therapies will be needed. Here, we describe small-molecule inhibitors for SARS-CoV-2 Mac1, which counters ADP-ribosylation-mediated innate immune responses. Three high-throughput screening hits had the same 2-amide-3-methylester thiophene scaffold. We studied the compound binding mode using X-ray crystallography, allowing us to design analogues. Compound 27 (MDOLL-0229) had an IC50 of 2.1 μM and was selective for CoV Mac1 proteins after profiling for activity against a panel of viral and human proteins. The improved potency allowed testing of its effect on virus replication, and indeed, 27 inhibited replication of both murine hepatitis virus (MHV) prototypes CoV and SARS-CoV-2. Sequencing of a drug-resistant MHV identified mutations in Mac1, further demonstrating the specificity of 27. Compound 27 is the first Mac1-targeted small molecule demonstrated to inhibit coronavirus replication in a cell model.

Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search

Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding-SmallSA-for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.

Advanced exploitation of unmerged reflection data during processing and refinement with autoPROC and BUSTER

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Crystal structure and biochemical activity of the macrodomain from rubella virus p150

Rubella virus encodes a nonstructural polyprotein with RNA polymerase, methyltransferase, and papain-like cysteine protease activities, along with a putative macrodomain of unknown function. Macrodomains bind ADP-ribose adducts, a post-translational modification that plays a key role in host-virus conflicts. Some macrodomains can also remove the mono-ADP-ribose adduct or degrade poly-ADP-ribose chains. Here, we report high-resolution crystal structures of the macrodomain from rubella virus nonstructural protein p150, with and without ADP-ribose binding. The overall fold is most similar to macroD-type macrodomains from various nonviral species. The specific composition and structure of the residues that coordinate ADP-ribose in the rubella virus macrodomain are most similar to those of macrodomains from alphaviruses. Isothermal calorimetry shows that the rubella virus macrodomain binds ADP-ribose in solution. Enzyme assays show that the rubella virus macrodomain can hydrolyze both mono- and poly-ADP-ribose adducts. Site-directed mutagenesis identifies Asn39 and Cys49 required for mono-ADP-ribosylhydrolase (de-MARylation) activity.IMPORTANCERubella virus remains a global health threat. Rubella infections during pregnancy can cause serious congenital pathology, for which no antiviral treatments are available. Our work demonstrates that, like alpha- and coronaviruses, rubiviruses encode a mono-ADP-ribosylhydrolase with a structurally conserved macrodomain fold to counteract MARylation by poly (ADP-ribose) polymerases (PARPs) in the host innate immune response. Our structural data will guide future efforts to develop novel antiviral therapeutics against rubella or infections with related viruses.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

An expanded view of ligandability in the allosteric enzyme PTP1B from computational reanalysis of large-scale crystallographic data

The recent advent of crystallographic small-molecule fragment screening presents the opportunity to obtain unprecedented numbers of ligand-bound protein crystal structures from a single high-throughput experiment, mapping ligandability across protein surfaces and identifying useful chemical footholds for structure-based drug design. However, due to the low binding affinities of most fragments, detecting bound fragments from crystallographic datasets has been a challenge. Here we report a trove of 65 new fragment hits across 59 new liganded crystal structures for PTP1B, an "undruggable" therapeutic target enzyme for diabetes and cancer. These structures were obtained from computational analysis of data from a large crystallographic screen, demonstrating the power of this approach to elucidate many (~50% more) "hidden" ligand-bound states of proteins. Our new structures include a fragment hit found in a novel binding site in PTP1B with a unique location relative to the active site, one that validates another new binding site recently identified by simulations, one that links adjacent allosteric sites, and, perhaps most strikingly, a fragment that induces long-range allosteric protein conformational responses via a previously unreported intramolecular conduit. Altogether, our research highlights the utility of computational analysis of crystallographic data, makes publicly available dozens of new ligand-bound structures of a high-value drug target, and identifies novel aspects of ligandability and allostery in PTP1B.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Targeting SARS-CoV-2 nonstructural protein 3: Function, structure, inhibition, and perspective in drug discovery

As a highly contagious human pathogen, severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) has infected billions of people worldwide with more than 6 million deaths. With several effective vaccines and antiviral drugs now available, the SARS-CoV-2 pandemic been brought under control. However, a new pathogenic coronavirus could emerge in the future, given the zoonotic nature of this virus. Natural evolution and drug-induced mutations of SARS-CoV-2 also require continued efforts for new anti-coronavirus drugs. Nonstructural protein (nsp) 3 of CoVs is a large, multifunctional protein, containing a papain-like protease (PLpro) and a macrodomain (Mac1), which are essential for viral replication. Here, we provide a comprehensive review of the function, structure, and inhibition of SARS-CoV/-CoV-2 PLpro and Mac1. We also discuss advances in, and challenges to, the discovery of drugs against these targets.

Discovery of 2-amide-3-methylester thiophenes that target SARS-CoV-2 Mac1 and repress coronavirus replication, validating Mac1 as an anti-viral target

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has made it clear that further development of antiviral therapies will be needed to combat additional SARS-CoV-2 variants or novel CoVs. Here, we describe small molecule inhibitors for SARS-CoV-2 Mac1, which counters ADP-ribosylation mediated innate immune responses. The compounds inhibiting Mac1 were discovered through high-throughput screening (HTS) using a protein FRET-based competition assay and the best hit compound had an IC50 of 14 μM. Three validated HTS hits have the same 2-amide-3-methylester thiophene scaffold and the scaffold was selected for structure-activity relationship (SAR) studies through commercial and synthesized analogs. We studied the compound binding mode in detail using X-ray crystallography and this allowed us to focus on specific features of the compound and design analogs. Compound 27 (MDOLL-0229) had an IC50 of 2.1 μM and was generally selective for CoV Mac1 proteins after profiling for activity against a panel of viral and human ADP-ribose binding proteins. The improved potency allowed testing of its effect on virus replication and indeed, 27 inhibited replication of both MHVa prototype CoV, and SARS-CoV-2. Furthermore, sequencing of a drug-resistant MHV identified mutations in Mac1, further demonstrating the specificity of 27. Compound 27 is the first Mac1 targeted small molecule demonstrated to inhibit coronavirus replication in a cell model. This, together with its well-defined binding mode, makes 27 a good candidate for further hit/lead-optimization efforts.

Protocol for performing and optimizing differential scanning fluorimetry experiments

Differential scanning fluorimetry (DSF) is a widely used technique for determining the apparent melting temperature (Tma) of a purified protein. Here, we present a protocol for performing and optimizing DSF experiments. We describe steps for designing and performing the experiment, analyzing data, and optimization. We provide benchmarks for typical Tmas and ΔTmas, standard assay conditions, and upper and lower limits of commonly altered experimental variables. We also detail common pitfalls of DSF and ways to avoid, identify, and overcome them.

Preparing for the next pandemic

New lead drugs to treat COVID-19 are beginning to emerge.

An Update on the Current State of SARS-CoV-2 Mac1 Inhibitors

Non-structural protein 3 (nsp3) from all coronaviruses (CoVs) contains a conserved macrodomain, known as Mac1, that has been proposed as a potential therapeutic target for CoVs due to its critical role in viral pathogenesis. Mac1 is an ADP-ribose binding protein and ADP-ribosylhydrolase that promotes replication and blocks IFN responses, though the precise mechanisms it uses to carry out these functions remain unknown. Over the past 3 years following the onset of COVID-19, several groups have used high-throughput screening with multiple assays and chemical modifications to create unique chemical inhibitors of the SARS-CoV-2 Mac1 protein. Here, we summarize the current efforts to identify selective and potent inhibitors of SARS-CoV-2 Mac1.

SARS-CoV-2 Mac1 is required for IFN antagonism and efficient virus replication in cell culture and in mice

Several coronavirus (CoV) encoded proteins are being evaluated as targets for antiviral therapies for COVID-19. Included in these drug targets is the conserved macrodomain, or Mac1, an ADP-ribosylhydrolase and ADP-ribose binding protein encoded as a small domain at the N terminus of nonstructural protein 3. Utilizing point mutant recombinant viruses, Mac1 was shown to be critical for both murine hepatitis virus (MHV) and severe acute respiratory syndrome (SARS)-CoV virulence. However, as a potential drug target, it is imperative to understand how a complete Mac1 deletion impacts the replication and pathogenesis of different CoVs. To this end, we created recombinant bacterial artificial chromosomes (BACs) containing complete Mac1 deletions (ΔMac1) in MHV, MERS-CoV, and SARS-CoV-2. While we were unable to recover infectious virus from MHV or MERS-CoV ΔMac1 BACs, SARS-CoV-2 ΔMac1 was readily recovered from BAC transfection, indicating a stark difference in the requirement for Mac1 between different CoVs. Furthermore, SARS-CoV-2 ΔMac1 replicated at or near wild-type levels in multiple cell lines susceptible to infection. However, in a mouse model of severe infection, ΔMac1 was quickly cleared causing minimal pathology without any morbidity. ΔMac1 SARS-CoV-2 induced increased levels of interferon (IFN) and IFN-stimulated gene expression in cell culture and mice, indicating that Mac1 blocks IFN responses which may contribute to its attenuation. ΔMac1 infection also led to a stark reduction in inflammatory monocytes and neutrophils. These results demonstrate that Mac1 only minimally impacts SARS-CoV-2 replication, unlike MHV and MERS-CoV, but is required for SARS-CoV-2 pathogenesis and is a unique antiviral drug target.

A single inactivating amino acid change in the SARS-CoV-2 NSP3 Mac1 domain attenuates viral replication in vivo

Despite unprecedented efforts, our therapeutic arsenal against SARS-CoV-2 remains limited. The conserved macrodomain 1 (Mac1) in NSP3 is an enzyme exhibiting ADP-ribosylhydrolase activity and a possible drug target. To determine the role of Mac1 catalytic activity in viral replication, we generated recombinant viruses and replicons encoding a catalytically inactive NSP3 Mac1 domain by mutating a critical asparagine in the active site. While substitution to alanine (N40A) reduced catalytic activity by ~10-fold, mutations to aspartic acid (N40D) reduced activity by ~100-fold relative to wild-type. Importantly, the N40A mutation rendered Mac1 unstable in vitro and lowered expression levels in bacterial and mammalian cells. When incorporated into SARS-CoV-2 molecular clones, the N40D mutant only modestly affected viral fitness in immortalized cell lines, but reduced viral replication in human airway organoids by 10-fold. In mice, the N40D mutant replicated at >1000-fold lower levels compared to the wild-type virus while inducing a robust interferon response; all animals infected with the mutant virus survived infection. Our data validate the critical role of SARS-CoV-2 NSP3 Mac1 catalytic activity in viral replication and as a promising therapeutic target to develop antivirals.

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

Realizing integration in structural biology: The 2022 ISBUC Annual Meeting

This meeting report presents the 2022 Annual Meeting of the cluster for Integrative Structural Biology at the University of Copenhagen (ISBUC) and discusses the cluster approach to interdisciplinary research management. This approach successfully facilitates cross-faculty and inter-departmental collaboration. Innovative integrative research collaborations ignited by ISBUC, as well as research presented at the meeting, are showcased.

Accelerating antiviral drug discovery: lessons from COVID-19

During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.