Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

A small molecule exerts selective antiviral activity by targeting the human cytomegalovirus nuclear egress complex.

Human cytomegalovirus (HCMV) is an important pathogen for which new antiviral drugs are needed. HCMV, like other herpesviruses, encodes a nuclear egress complex (NEC) composed of two subunits, UL50 and UL53, whose interaction is crucial for viral replication. To explore whether small molecules can exert selective antiviral activity by inhibiting NEC subunit interactions, we established a homogeneous time-resolved fluorescence (HTRF) assay of these interactions and used it to screen >200,000 compound-containing wells. Two compounds, designated GK1 and GK2, which selectively inhibited this interaction in the HTRF assay with GK1 also active in a co-immunoprecipitation assay, exhibited more potent anti-HCMV activity than cytotoxicity or activity against another herpesvirus. At doses that substantially reduced HCMV plaque formation, GK1 and GK2 had little or no effect on the expression of viral proteins and reduced the co-localization of UL53 with UL50 at the nuclear rim in a subset of cells. GK1 and GK2 contain an acrylamide moiety predicted to covalently interact with cysteines, and an analog without this potential lacked activity. Mass spectrometric analysis showed binding of GK2 to multiple cysteines on UL50 and UL53. Nevertheless, substitution of cysteine 214 of UL53 with serine (C214S) ablated detectable inhibitory activity of GK1 and GK2 in vitro, and the C214S substitution engineered into HCMV conferred resistance to GK1, the more potent of the two inhibitors. Thus, GK1 exerts selective antiviral activity by targeting the NEC. Docking studies suggest that the acrylamide tethers one end of GK1 or GK2 to C214 within a pocket of UL53, permitting the other end of the molecule to sterically hinder UL50 to prevent NEC formation. Our results prove the concept that targeting the NEC with small molecules can selectively block HCMV replication. Such compounds could serve as a foundation for development of anti-HCMV drugs and as chemical tools for studying HCMV.

Targeting ROS production through inhibition of NADPH oxidases.

NADPH oxidases (NOXs) are transmembrane enzymes that are devoted to the production of reactive oxygen species (ROS). In cancers, dysregulation of NOX enzymes affects ROS production, leading to redox unbalance and tumor progression. Consequently, NOXs are a drug target for cancer therapeutics, although current therapies have off-target effects: there is a need for isoenzyme-selective inhibitors. Here, we describe fully validated human NOX inhibitors, obtained from an in silico screen, targeting the active site of Cylindrospermum stagnale NOX5 (csNOX5). The hits are validated by in vitro and in cellulo enzymatic and binding assays, and their binding modes to the dehydrogenase domain of csNOX5 studied via high-resolution crystal structures. A high-throughput screen in a panel of cancer cells shows activity in selected cancer cell lines and synergistic effects with KRAS modulators. Our work lays the foundation for the development of inhibitor-based methods for controlling the tightly regulated and highly localized ROS sources.

Novel multi-objective affinity approach allows to identify pH-specific µ-opioid receptor agonists.

Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the [Formula: see text]-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported [Formula: see text]-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.

Identification of small molecule antivirals against HTLV-1 by targeting the hDLG1-Tax-1 protein-protein interaction.

Human T-cell leukemia virus type-1 (HTLV-1) is the first pathogenic retrovirus discovered in human. Although HTLV-1-induced diseases are well-characterized and linked to the encoded Tax-1 oncoprotein, there is currently no strategy to target Tax-1 functions with small molecules. Here, we analyzed the binding of Tax-1 to the human homolog of the drosophila discs large tumor suppressor (hDLG1/SAP97), a multi-domain scaffolding protein involved in Tax-1-transformation ability. We have solved the structures of the PDZ binding motif (PBM) of Tax-1 in complex with the PDZ1 and PDZ2 domains of hDLG1 and assessed the binding of 10 million molecules by virtual screening. Among the 19 experimentally confirmed compounds, one systematically inhibited the Tax-1-hDLG1 interaction in different biophysical and cellular assays, as well as HTLV-1 cell-to-cell transmission in a T-cell model. Thus, our work demonstrates that interactions involving Tax-1 PDZ-domains are amenable to small-molecule inhibition, which provides a framework for the design of targeted therapies for HTLV-1-induced diseases.

Irisin acts through its integrin receptor in a two-step process involving extracellular Hsp90α.

Exercise benefits the human body in many ways. Irisin is secreted by muscle, increased with exercise, and conveys physiological benefits, including improved cognition and resistance to neurodegeneration. Irisin acts via αV integrins; however, a mechanistic understanding of how small polypeptides like irisin can signal through integrins is poorly understood. Using mass spectrometry and cryo-EM, we demonstrate that the extracellular heat shock protein 90α (eHsp90α) is secreted by muscle with exercise and activates integrin αVß5. This allows for high-affinity irisin binding and signaling through an Hsp90α/αV/ß5 complex. By including hydrogen/deuterium exchange data, we generate and experimentally validate a 2.98 Å RMSD irisin/αVß5 complex docking model. Irisin binds very tightly to an alternative interface on αVß5 distinct from that used by known ligands. These data elucidate a non-canonical mechanism by which a small polypeptide hormone like irisin can function through an integrin receptor.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches.

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Challenges for chemistry in Ukraine after the war: Ukrainian science requires rebuilding and support.

The unprovoked Russian invasion has created considerable challenges for Ukrainian science. In this article, we discuss actions needed to support and rebuild Ukrainian science and educational systems. The proposed actions take into account past Ukrainian scientific achievements including developments in organic chemistry.

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.

Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits.

Mpro, the main protease of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is essential for the viral life cycle. Accordingly, several groups have performed in silico screens to identify Mpro inhibitors that might be used to treat SARS-CoV-2 infections. We selected more than five hundred compounds from the top-ranking hits of two very large in silico screens for on-demand synthesis. We then examined whether these compounds could bind to Mpro and inhibit its protease activity. Two interesting chemotypes were identified, which were further evaluated by characterizing an additional five hundred synthesis on-demand analogues. The compounds of the first chemotype denatured Mpro and were considered not useful for further development. The compounds of the second chemotype bound to and enhanced the melting temperature of Mpro. The most active compound from this chemotype inhibited Mpro in vitro with an IC50 value of 1 µM and suppressed replication of the SARS-CoV-2 virus in tissue culture cells. Its mode of binding to Mpro was determined by X-ray crystallography, revealing that it is a non-covalent inhibitor. We propose that the inhibitors described here could form the basis for medicinal chemistry efforts that could lead to the development of clinically relevant inhibitors.

VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.

A biphenyl inhibitor of eIF4E targeting an internal binding site enables the design of cell-permeable PROTAC-degraders.

The eukaryotic translation initiation factor 4E (eIF4E) is the master regulator of cap-dependent protein synthesis. Overexpression of eIF4E is implicated in diseases such as cancer, where dysregulation of oncogenic protein translation is frequently observed. eIF4E has been an attractive target for cancer treatment. Here we report a high-resolution X-ray crystal structure of eIF4E in complex with a novel inhibitor (i4EG-BiP) that targets an internal binding site, in contrast to the previously described inhibitor, 4EGI-1, which binds to the surface. We demonstrate that i4EG-BiP is able to displace the scaffold protein eIF4G and inhibit the proliferation of cancer cells. We provide insights into how i4EG-BiP is able to inhibit cap-dependent translation by increasing the eIF4E-4E-BP1 interaction while diminishing the interaction of eIF4E with eIF4G. Leveraging structural details, we designed proteolysis targeted chimeras (PROTACs) derived from 4EGI-1 and i4EG-BiP and characterized these on biochemical and cellular levels. We were able to design PROTACs capable of binding eIF4E and successfully engaging Cereblon, which targets proteins for proteolysis. However, these initial PROTACs did not successfully stimulate degradation of eIF4E, possibly due to competitive effects from 4E-BP1 binding. Our results highlight challenges of targeted proteasomal degradation of eIF4E that must be addressed by future efforts.

Cryo-EM structure of an activated GPCR-G protein complex in lipid nanodiscs.

G-protein-coupled receptors (GPCRs) are the largest superfamily of transmembrane proteins and the targets of over 30% of currently marketed pharmaceuticals. Although several structures have been solved for GPCR-G protein complexes, few are in a lipid membrane environment. Here, we report cryo-EM structures of complexes of neurotensin, neurotensin receptor 1 and Gαi1ß1γ1 in two conformational states, resolved to resolutions of 4.1 and 4.2 Å. The structures, determined in a lipid bilayer without any stabilizing antibodies or nanobodies, reveal an extended network of protein-protein interactions at the GPCR-G protein interface as compared to structures obtained in detergent micelles. The findings show that the lipid membrane modulates the structure and dynamics of complex formation and provide a molecular explanation for the stronger interaction between GPCRs and G proteins in lipid bilayers. We propose an allosteric mechanism for GDP release, providing new insights into the activation of G proteins for downstream signaling.

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.

The unparalleled global effort to combat the continuing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic over the last year has resulted in promising prophylactic measures. However, a need still exists for cheap, effective therapeutics, and targeting multiple points in the viral life cycle could help tackle the current, as well as future, coronaviruses. Here, we leverage our recently developed, ultra-large-scale in silico screening platform, VirtualFlow, to search for inhibitors that target SARS-CoV-2. In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions.

A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.

An open-source drug discovery platform enables ultra-large virtual screens.

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method.

Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.

N-Acyl Homoserine Lactone Analog Modulators of the Pseudomonas aeruginosa Rhll Quorum Sensing Signal Synthase.

Virulence in the Gram-negative pathogen Pseudomonas aeruginosa relies in part on the efficient functioning of two LuxI/R dependent quorum sensing (QS) cascades, namely, the LasI/R and RhlI/R systems that generate and respond to N-(3-oxo)-dodecanoyl-l-homoserine lactone and N-butyryl-l-homoserine lactone, respectively. The two acyl homoserine lactone (AHL) synthases, LasI and RhlI, use 3-oxododecanoyl-ACP and butyryl-ACP, respectively, as the acyl-substrates to generate the corresponding autoinducer signals for the bacterium. Although AHL synthases represent excellent targets for developing QS modulators in P. aeruginosa, and in other related bacteria, the identification of potent and signal synthase specific inhibitors has represented a significant technical challenge. In the current study, we sought to test the utility of AHL analogs as potential modulators of an AHL synthase and selected RhlI in P. aeruginosa as an initial target. We systematically varied the chemical functionalities of the AHL headgroup, acyl chain tail, and head-to-tail linkage to construct a small library of signal analogs and evaluated them for RhlI modulatory activity. Although the native N-butyryl-l-homoserine lactone did not inhibit RhlI, we discovered that several of our long-chain, unsubstituted acyl-d-homoserine lactones and acyl-d-homocysteine thiolactones inhibited while a few of the 3-oxoacyl-chain counterparts activated the enzyme. Additional mechanistic investigations with acyl-substrate analogs and docking experiments with AHL analogs revealed two distinct inhibitor and activator binding pockets in the enzyme. This study provides the first evidence of the yet untapped potential of AHL analogs as signal synthase modulators of QS pathways.