A novel class of broad-spectrum active-site-directed 3C-like protease inhibitors with nanomolar antiviral activity against highly immune-evasive SARS-CoV-2 Omicron subvariants.

Antivirals with broad coronavirus activity are important for treating high-risk individuals exposed to the constantly evolving SARS-CoV-2 variants of concern (VOCs) as well as emerging drug-resistant variants. We developed and characterized a novel class of active-site-directed 3-chymotrypsin-like protease (3CLpro) inhibitors (C2-C5a). Our lead direct-acting antiviral (DAA), C5a, is a non-covalent, non-peptide with a dissociation constant of 170 nM against recombinant SARS-CoV-2 3CLpro. The compounds C2-C5a exhibit broad-spectrum activity against Omicron subvariants (BA.5, BQ.1.1, and XBB.1.5) and seasonal human coronavirus-229E infection in human cells. Notably, C5a has median effective concentrations of 30-50 nM against BQ.1.1 and XBB.1.5 in two different human cell lines. X-ray crystallography has confirmed the unique binding modes of C2-C5a to the 3CLpro, which can limit virus cross-resistance to emerging Paxlovid-resistant variants. We tested the effect of C5a with two of our newly discovered host-directed antivirals (HDAs): N-0385, a TMPRSS2 inhibitor, and bafilomycin D (BafD), a human vacuolar H+-ATPase [V-ATPase] inhibitor. We demonstrated a synergistic action of C5a in combination with N-0385 and BafD against Omicron BA.5 infection in human Calu-3 lung cells. Our findings underscore that a SARS-CoV-2 multi-targeted treatment for circulating Omicron subvariants based on DAAs (C5a) and HDAs (N-0385 or BafD) can lead to therapeutic benefits by enhancing treatment efficacy. Furthermore, the high-resolution structures of SARS-CoV-2 3CLpro in complex with C2-C5a will facilitate future rational optimization of our novel broad-spectrum active-site-directed 3C-like protease inhibitors.

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 µM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.

Targeting SARS-CoV-2 papain-like protease in the postvaccine era.

While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs.

X-ray crystallographic characterization of the SARS-CoV-2 main protease polyprotein cleavage sites essential for viral processing and maturation.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the pathogen that causes COVID-19, produces polyproteins 1a and 1ab that contain, respectively, 11 or 16 non-structural proteins (nsp). Nsp5 is the main protease (Mpro) responsible for cleavage at eleven positions along these polyproteins, including at its own N- and C-terminal boundaries, representing essential processing events for viral assembly and maturation. Using C-terminally substituted Mpro chimeras, we have determined X-ray crystallographic structures of Mpro in complex with 10 of its 11 viral cleavage sites, bound at full occupancy intermolecularly in trans, within the active site of either the native enzyme and/or a catalytic mutant (C145A). Capture of both acyl-enzyme intermediate and product-like complex forms of a P2(Leu) substrate in the native active site provides direct comparative characterization of these mechanistic steps as well as further informs the basis for enhanced product release of Mpro's own unique C-terminal P2(Phe) cleavage site to prevent autoinhibition. We characterize the underlying noncovalent interactions governing binding and specificity for this diverse set of substrates, showing remarkable plasticity for subsites beyond the anchoring P1(Gln)-P2(Leu/Val/Phe), representing together a near complete analysis of a multiprocessing viral protease. Collectively, these crystallographic snapshots provide valuable mechanistic and structural insights for antiviral therapeutic development.

Development of VPC-70619, a Small-Molecule N-Myc Inhibitor as a Potential Therapy for Neuroendocrine Prostate Cancer.

The Myc family of transcription factors are involved in the development and progression of numerous cancers, including prostate cancer (PCa). Under the pressure of androgen receptor (AR)-directed therapies resistance can occur, leading to the lethal form of PCa known as neuroendocrine prostate cancer (NEPC), characterized among other features by N-Myc overexpression. There are no clinically approved treatments for NEPC, translating into poor patient prognosis and survival. Therefore, there is a pressing need to develop novel therapeutic avenues to treat NEPC patients. In this study, we investigate the N-Myc-Max DNA binding domain (DBD) as a potential target for small molecule inhibitors and utilize computer-aided drug design (CADD) approaches to discover prospective hits. Through further exploration and optimization, a compound, VPC-70619, was identified with notable anti-N-Myc potency and strong antiproliferative activity against numerous N-Myc expressing cell lines, including those representing NEPC.

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.

Drugging the 'undruggable'. Therapeutic targeting of protein-DNA interactions with the use of computer-aided drug discovery methods.

Transcription factors (TFs) act as major oncodrivers in many cancers and are frequently regarded as high-value therapeutic targets. The functionality of TFs relies on direct protein-DNA interactions, which are notoriously difficult to target with small molecules. However, this prior view of the 'undruggability' of protein-DNA interfaces has shifted substantially in recent years, in part because of significant advances in computer-aided drug discovery (CADD). In this review, we highlight recent examples of successful CADD campaigns resulting in drug candidates that directly interfere with protein-DNA interactions of several key cancer TFs, including androgen receptor (AR), ETS-related gene (ERG), MYC, thymocyte selection-associated high mobility group box protein (TOX), topoisomerase II (TOP2), and signal transducer and activator of transcription 3 (STAT3). Importantly, these findings open novel and compelling avenues for therapeutic targeting of over 1600 human TFs implicated in many conditions including and beyond cancer.

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules.

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the pathogen that causes the disease COVID-19, produces replicase polyproteins 1a and 1ab that contain, respectively, 11 or 16 nonstructural proteins (nsp). Nsp5 is the main protease (Mpro) responsible for cleavage at eleven positions along these polyproteins, including at its own N- and C-terminal boundaries, representing essential processing events for subsequent viral assembly and maturation. We have determined X-ray crystallographic structures of this cysteine protease in its wild-type free active site state at 1.8 Å resolution, in its acyl-enzyme intermediate state with the native C-terminal autocleavage sequence at 1.95 Å resolution and in its product bound state at 2.0 Å resolution by employing an active site mutation (C145A). We characterize the stereochemical features of the acyl-enzyme intermediate including critical hydrogen bonding distances underlying catalysis in the Cys/His dyad and oxyanion hole. We also identify a highly ordered water molecule in a position compatible for a role as the deacylating nucleophile in the catalytic mechanism and characterize the binding groove conformational changes and dimerization interface that occur upon formation of the acyl-enzyme. Collectively, these crystallographic snapshots provide valuable mechanistic and structural insights for future antiviral therapeutic development including revised molecular docking strategies based on Mpro inhibition.

Dual-Inhibitors of N-Myc and AURKA as Potential Therapy for Neuroendocrine Prostate Cancer.

Resistance to androgen-receptor (AR) directed therapies is, among other factors, associated with Myc transcription factors that are involved in development and progression of many cancers. Overexpression of N-Myc protein in prostate cancer (PCa) leads to its transformation to advanced neuroendocrine prostate cancer (NEPC) that currently has no approved treatments. N-Myc has a short half-life but acts as an NEPC stimulator when it is stabilized by forming a protective complex with Aurora A kinase (AURKA). Therefore, dual-inhibition of N-Myc and AURKA would be an attractive therapeutic avenue for NEPC. Following our computer-aided drug discovery approach, compounds exhibiting potent N-Myc specific inhibition and strong anti-proliferative activity against several N-Myc driven cell lines, were identified. Thereafter, we have developed dual inhibitors of N-Myc and AURKA through structure-based drug design approach by merging our novel N-Myc specific chemical scaffolds with fragments of known AURKA inhibitors. Favorable binding modes of the designed compounds to both N-Myc and AURKA target sites have been predicted by docking. A promising lead compound, 70812, demonstrated low-micromolar potency against both N-Myc and AURKA in vitro assays and effectively suppressed NEPC cell growth.

Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery.

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure-activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.

Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3†Billion Compounds.

The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.

A functional substitution in the L-aromatic amino acid decarboxylase enzyme worsens somatic symptoms via a serotonergic pathway.

OBJECTIVE: Heightened somatic symptoms are reported by a wide range of patients with chronic pain and have been associated with emotional distress and physical dysfunction. Despite their clinical significance, molecular mechanisms leading to their manifestation are not understood. METHODS: We used an association study design based on a curated list of 3,295 single nucleotide polymorphisms mapped to 358 genes to test somatic symptoms reporting using the Pennebaker Inventory of Limbic Languidness questionnaire from a case-control cohort of orofacial pain (n = 1,607). A replication meta-analysis of 3 independent cohorts (n = 3,189) was followed by functional validation, including in silico molecular dynamics, in vitro enzyme assays, and measures of serotonin (5-HT) plasma concentration. RESULTS: An association with the T allele of rs11575542 coding for an arginine to glutamine substitution in the L-aromatic amino acid decarboxylase (AADC) enzyme was replicated in a meta-analysis of 3 independent cohorts. In a combined meta-analysis of all cohorts, this association reached p = 6.43 × 10-8 . In silico studies demonstrated that this substitution dramatically reduces the conformational dynamics of AADC, potentially lowering its binding capacity to a cofactor. in vitro enzymatic assays showed that this substitution reduces the maximum kinetic velocity of AADC, hence lowering 5-HT levels. Finally, plasma samples from 90 subjects showed correlation between low 5-HT levels and heightened somatic symptoms. INTERPRETATION: Using functional genomics approaches, we identified a polymorphism in the AADC enzyme that contributes to somatic symptoms through reduced levels of 5-HT. Our findings suggest a molecular mechanism underlying the pathophysiology of somatic symptoms and opens up new treatment options targeting the serotonergic system. ANN NEUROL 2019;86:168-180.

Molecular basis of interactions between SH3 domain-containing proteins and the proline-rich region of the ubiquitin ligase Itch.

The ligase Itch plays major roles in signaling pathways by inducing ubiquitylation-dependent degradation of several substrates. Substrate recognition and binding are critical for the regulation of this reaction. Like closely related ligases, Itch can interact with proteins containing a PPXY motif via its WW domains. In addition to these WW domains, Itch possesses a proline-rich region (PRR) that has been shown to interact with several Src homology 3 (SH3) domain-containing proteins. We have previously established that despite the apparent surface uniformity and conserved fold of SH3 domains, they display different binding mechanisms and affinities for their interaction with the PRR of Itch. Here, we attempt to determine the molecular bases underlying the wide range of binding properties of the Itch PRR. Using pulldown assays combined with mass spectrometry analysis, we show that the Itch PRR preferentially forms complexes with endophilins, amphyphisins, and pacsins but can also target a variety of other SH3 domain-containing proteins. In addition, we map the binding sites of these proteins using a combination of PRR sub-sequences and mutants. We find that different SH3 domains target distinct proline-rich sequences overlapping significantly. We also structurally analyze these protein complexes using crystallography and molecular modeling. These structures depict the position of Itch PRR engaged in a 1:2 protein complex with ß-PIX and a 1:1 complex with the other SH3 domain-containing proteins. Taken together, these results reveal the binding preferences of the Itch PRR toward its most common SH3 domain-containing partners and demonstrate that the PRR region is sufficient for binding.