Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Clinically-observed FOXA1 mutations upregulate SEMA3C through transcriptional derepression in prostate cancer

FOXA1 is a pioneer transcription factor that is frequently mutated in prostate, breast, bladder, and salivary gland malignancies. Indeed, metastatic castration-resistant prostate cancer (mCRPC) commonly harbour FOXA1 mutations with a prevalence of 35%. However, despite the frequent recurrence of FOXA1 mutations in prostate cancer, the mechanisms by which FOXA1 variants drive its oncogenic effects are still unclear. Semaphorin 3C (SEMA3C) is a secreted autocrine growth factor that drives growth and treatment resistance of prostate and other cancers and is known to be regulated by both AR and FOXA1. In the present study, we characterize FOXA1 alterations with respect to its regulation of SEMA3C. Our findings reveal that FOXA1 alterations lead to elevated levels of SEMA3C both in prostate cancer specimens and in vitro. We further show that FOXA1 negatively regulates SEMA3C via intronic cis elements, and that mutations in FOXA1 forkhead domain attenuate its inhibitory function in reporter assays, presumably by disrupting DNA binding of FOXA1. Our findings underscore the key role of FOXA1 in prostate cancer progression and treatment resistance by regulating SEMA3C expression and suggest that SEMA3C may be a driver of growth and tumor vulnerability of mCRPC harboring FOXA1 alterations.

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

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.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

Dependency of Tamoxifen Sensitive and Resistant ER+ Breast Cancer Cells on Semaphorin 3C (SEMA3C) for Growth

Estrogen receptor positive (ER+) breast cancer (BCa) accounts for the highest proportion of breast cancer-related deaths. While endocrine therapy is highly effective for this subpopulation, endocrine resistance remains a major challenge and the identification of novel targets is urgently needed. Previously, we have shown that Semaphorin 3C (SEMA3C) is an autocrine growth factor that drives the growth and treatment resistance of various cancers, but its role in breast cancer progression and endocrine resistance is poorly understood. Here, we report that SEMA3C plays a role in maintaining the growth of ER+ BCa cells and is a novel, tractable therapeutic target for the treatment of ER+ BCa patients. Analyses of publicly available clinical datasets indicate that ER+ BCa patients express significantly higher levels of SEMA3C mRNA than other subtypes. Furthermore, SEMA3C mRNA expression was positively correlated with ESR1 mRNA expression. ER+ BCa cell lines (MCF7 and T47D) expressed higher levels of SEMA3C mRNA and protein than a normal mammary epithelial MCF10A cell line. ER siRNA knockdown was suppressed, while dose-dependent beta-estradiol treatment induced SEMA3C expression in both MCF7 and T47D cells, suggesting that SEMA3C is an ER-regulated gene. The stimulation of ER+ BCa cells with recombinant SEMA3C activated MAPK and AKT signaling in a dose-dependent manner. Conversely, SEMA3C silencing inhibited Estrogen Receptor (ER) expression, MAPK and AKT signaling pathways while simultaneously inducing apoptosis, as monitored by flow cytometry and Western blot analyses. SEMA3C silencing significantly inhibited the growth of ER+ BCa cells, implicating a growth dependency of ER+ BCa cells on SEMA3C. Moreover, the analysis of tamoxifen resistant (TamR) cell models (TamC3 and TamR3) showed that SEMA3C levels remain high despite treatment with tamoxifen. Tamoxifen-resistant cells remained dependent on SEMA3C for growth and survival. Treatment with B1SP Fc fusion protein, a SEMA3C pathway inhibitor, attenuated SEMA3C-induced signaling and growth across a panel of tamoxifen sensitive and resistant ER+ breast cancer cells. Furthermore, SEMA3C silencing and B1SP treatment were associated with decreased EGFR signaling in TamR cells. Here, our study implicates SEMA3C in a functional role in ER+ breast cancer signaling and growth that suggests ER+ BCa patients may benefit from SEMA3C-targeted therapy.

Abstract 3079: Characterization of ER-AF2 inhibitors in breast cancer

Up to 80% of breast cancers (BCa) rely on the estrogen receptor (ER) for their growth and progression. This dependence on ER has led to many hormonal therapies that target this receptor. However, almost 40% of these cancers will acquire resistance over the course of the treatment period. One potential cause of resistance is mutations in the estrogen binding site (EBS) of ER. As such, there is an increasing need for novel inhibitors that targets ER at a site separate from the EBS. Here, we propose targeting the activation-function (AF2) pocket of ER that is important for cofactor binding and transcription activation. Billions of compounds were screened through an in-silico deep docking method, and potential AF2 inhibitors were then validated in cell-based and biophysical assays. We tested the effect of potential AF2 inhibitors on ER transcriptional activity using luciferase reporter assay in ER-positive T47D-kbluc cells. We then evaluated the effect of molecules on cell viability of ER-positive T47D and ER-negative MDA-MB-231 cells using PrestoBlue assays in order to exclude off-target effects. From these cell-based assays, we identified several inhibitors that effectively reduced transcriptional activity and viability in ER-positive T47D cells at low micromolar concentrations. We conducted PGC-1α peptide displacement assay to confirm their AF2 binding and estradiol displacement assays to exclude any binding to the EBS. Proximity ligation assay (PLA) showed disruption of the interaction between ER and coactivator SRC-3 upon treatment with ER-AF2 inhibitors in T47D cells. Current work focuses on confirming the direct binding between the compounds and recombinant ER-ligand binding domain by various biophysical assays (MST, BLI, and ITC). Future work aims to solve the structure of ER-LBD in a complex with our lead compound by X-ray crystallography. We predict that the use of potent ER-AF2 inhibitors along with current treatments, will provide a novel tactic that can act as a complementary therapeutic to target treatment resistance in ER+ BCa.

Citation Format: Jane Foo, Francesco Gentile, Joseph Lee, Helene Morin, Shabnam Massah, Maria Guo, Jason Smith, Fuqiang Ban, Artem Cherkasov, Nada Lallous. Characterization of ER-AF2 inhibitors in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3079.

PurificationDB: database of purification conditions for proteins

The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (https://purificationdatabase.herokuapp.com/), an open-access and user-friendly knowledge base that contains 4732 curated and standardized entries of protein purification conditions. Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis. Database URL https://purificationdatabase.herokuapp.com/.

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.

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by inclusion of this compound achieves the same differentiation efficiency with a 90% cost reduction. The presented in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.

Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Discovery of lead natural products for developing pan-SARS-CoV-2 therapeutics

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global public health crisis. The reduced efficacy of therapeutic monoclonal antibodies against emerging SARS-CoV-2 variants of concern (VOCs), such as omicron BA.5 subvariants, has underlined the need to explore a novel spectrum of antivirals that are effective against existing and evolving SARS-CoV-2 VOCs. To address the need for novel therapeutic options, we applied cell-based high-content screening to a library of natural products (NPs) obtained from plants, fungi, bacteria, and marine sponges, which represent a considerable diversity of chemical scaffolds. The antiviral effect of 373 NPs was evaluated using the mNeonGreen (mNG) reporter SARS-CoV-2 virus in a lung epithelial cell line (Calu-3). The screening identified 26 NPs with half-maximal effective concentrations (EC50) below 50 μM against mNG-SARS-CoV-2; 16 of these had EC50 values below 10 μM and three NPs (holyrine A, alotaketal C, and bafilomycin D) had EC50 values in the nanomolar range. We demonstrated the pan-SARS-CoV-2 activity of these three lead antivirals against SARS-CoV-2 highly transmissible Omicron subvariants (BA.5, BA.2 and BA.1) and highly pathogenic Delta VOCs in human Calu-3 lung cells. Notably, holyrine A, alotaketal C, and bafilomycin D, are potent nanomolar inhibitors of SARS-CoV-2 Omicron subvariants BA.5 and BA.2. The pan-SARS-CoV-2 activity of alotaketal C [protein kinase C (PKC) activator] and bafilomycin D (V-ATPase inhibitor) suggest that these two NPs are acting as host-directed antivirals (HDAs). Future research should explore whether PKC regulation impacts human susceptibility to and the severity of SARS-CoV-2 infection, and it should confirm the important role of human V-ATPase in the VOC lifecycle. Interestingly, we observed a synergistic action of bafilomycin D and N-0385 (a highly potent inhibitor of human TMPRSS2 protease) against Omicron subvariant BA.2 in human Calu-3 lung cells, which suggests that these two highly potent HDAs are targeting two different mechanisms of SARS-CoV-2 entry. Overall, our study provides insight into the potential of NPs with highly diverse chemical structures as valuable inspirational starting points for developing pan-SARS-CoV-2 therapeutics and for unravelling potential host factors and pathways regulating SARS-CoV-2 VOC infection including emerging omicron BA.5 subvariants.

Dynamic phase separation of the androgen receptor and its coactivators key to regulate gene expression

Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by specific genomic regions known as super-enhancers (SEs). The robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator mediator complex subunit 1 (MED1) to SEs in androgen-dependent PCa cells, thereby promoting oncogenic transcriptional programs. In this work, we reveal that full-length AR forms foci with liquid-like properties in different PCa models. We demonstrate that foci formation correlates with AR transcriptional activity, as this activity can be modulated by changing cellular foci content chemically or by silencing MED1. AR ability to phase separate was also validated in vitro by using recombinant full-length AR protein. We also demonstrate that AR antagonists, which suppress transcriptional activity by targeting key regions for homotypic or heterotypic interactions of this receptor, hinder foci formation in PCa cells and phase separation in vitro. Our results suggest that enhanced compartmentalization of AR and coactivators may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa.

Lin28 Regulates Cancer Cell Stemness for Tumour Progression

Tumours develop therapy resistance through complex mechanisms, one of which is that cancer stem cell (CSC) populations within the tumours present self-renewable capability and phenotypical plasticity to endure therapy-induced stress conditions and allow tumour progression to the therapy-resistant state. Developing therapeutic strategies to cope with CSCs requires a thorough understanding of the critical drivers and molecular mechanisms underlying the aforementioned processes. One such hub regulator of stemness is Lin28, an RNA-binding protein. Lin28 blocks the synthesis of let-7, a tumour-suppressor microRNA, and acts as a global regulator of cell differentiation and proliferation. Lin28also targets messenger RNAs and regulates protein translation. In this review, we explain the role of the Lin28/let-7 axis in establishing stemness, epithelial-to-mesenchymal transition, and glucose metabolism reprogramming. We also highlight the role of Lin28 in therapy-resistant prostate cancer progression and discuss the emergence of Lin28-targeted therapeutics and screening methods.

Structure-Based Study to Overcome Cross-Reactivity of Novel Androgen Receptor Inhibitors

The mutation-driven transformation of clinical anti-androgen drugs into agonists of the human androgen receptor (AR) represents a major challenge for the treatment of prostate cancer patients. To address this challenge, we have developed a novel class of inhibitors targeting the DNA-binding domain (DBD) of the receptor, which is distanced from the androgen binding site (ABS) targeted by all conventional anti-AR drugs and prone to resistant mutations. While many members of the developed 4-(4-phenylthiazol-2-yl)morpholine series of AR-DBD inhibitors demonstrated the effective suppression of wild-type AR, a few represented by 4-(4-(3-fluoro-2-methoxyphenyl)thiazol-2-yl)morpholine (VPC14368) exhibited a partial agonistic effect toward the mutated T878A form of the receptor, implying their cross-interaction with the AR ABS. To study the molecular basis of the observed cross-reactivity, we co-crystallized the T878A mutated form of the AR ligand binding domain (LBD) with a bound VPC14368 molecule. Computational modelling revealed that helix 12 of AR undergoes a characteristic shift upon VPC14368 binding causing the agonistic behaviour. Based on the obtained structural data we then designed derivatives of VPC14368 to successfully eliminate the cross-reactivity towards the AR ABS, while maintaining significant anti-AR DBD potency.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks

Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.

Targeting SARS-CoV-2 Papain-Like Protease in the Post-Vaccine 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.

Abstract 5283: Characterization of Gli activation by the estrogen receptor in breast cancer cells

Background: The nuclear steroid receptor superfamily encompasses a group of proteins best known fortheir functions as primary transcription factors that are conditionally active when bound to a ligand.Here, we show that a prominent member of this family, the estrogen receptor [ER-α] have a secondaryfunction of activating the Gli family of transcription factors. Gli is recognized as the mediator of activeHedgehog (Hh) signaling and plays an important role in cell development and growth. Gli activity isregulated by a post-translational proteolytic process that is suppressed by Hedgehog signaling.Previously we found that in prostate cancer, the ligand activated androgen receptor [AR] recognizes andbinds to Gli proteins at their Protein Processing Domains. This binding stabilizes Gli proteins in their un-proteolyzed active form and bypasses the Hedgehog signaling, thus promoting cancer progressionthrough non-canonical activation of Gli proteins. Due to the high similarity between AR and ER, wehypothesized that a similar Gli regulation could play a role in Breast Cancer (BrCA) progression. We thustested the ability of human ER-α to bind and activate Gli in BrCa and evaluated the role of this pathwayin tumor cell growth.

Methods: we measured Gli activity in 293T and BrCa cells (MCF7, T47D, MDA-MB-453) in presence andabsence of steroid ligand using Gli-luciferase reporter assay. We evaluated the interaction between Gli3and ER-α by co-immunoprecipitation and proximity ligation assay and assessed the stability of Gli3stability in BrCa cell extracts by western blots. We also studied the effect of ER-α knockdown ordestabilization (by fulvestrant treatment) on Gli3 stability, formation of intranuclear ER-α-Gli3complexes and Gli reporter activity. We also measured the expression level of Gli target genes in thepresence and absence of estradiol by qPCR in BrCa cells. Lastly, we evaluated the importance of Gli3expression on BrCa growth by Gli3 knockdown and Cyquant assay.

Results: We found that ER co-immunoprecipitates with Gli3. Transfection with ER-α increased Glireporter activity which was further increased by estradiol treatment. Acute (2hr) estradiol treatmentincreased intranuclear ER-α-Gli3 complex formation in BrCa cells. Chronic (48hr) estradiol treatmentincreased Gli3 stability and endogenous activity in BrCa cells. Destabilization or knockdown of ER-αdecreased estradiol-induced formation of ER-α-Gli3 complexes as well as Gli activity and stability in BrCacells. In addition, siRNA knockdown of Gli3 reduced growth in BrCa cells.

Conclusion: Collectively our results uncovered a new role of the steroid receptors ER and AR inregulating Gli oncogenic transcriptional activity in BrCa and PCa, respectively.

Citation Format: Shabnam Massah, Jane Foo, Na Li, Sarah Truong, Mannan Nouri, Lishi Xie, Gail Prins, Ralph Buttyan, Nada Lallous, Artem Cherkasov. Characterization of Gli activation by the estrogen receptor in breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5283.

Abstract 5729: Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression

Numerous cancers, including prostate cancer (PCa), have been shown to be addicted to transcription programs driven by specific genomic sites known as superenhancers (SEs). Recently, it has been proposed that the robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator MED1 to SEs in androgen-dependent prostate cancer cells, thereby promoting oncogenic transcriptional programs. In this work, we show that AR-rich, liquid-like foci form in prostate cancer models upon androgen stimulation. We reveal that foci formation correlates with AR transcriptional activity, which can be modulated by changing cellular foci content genetically or chemically. We also demonstrate that the transcriptional coactivator MED1 plays an essential role in the formation of transcriptionally active AR-rich foci and that AR antagonists that block cofactor recruitment or DNA binding hinder foci formation and thus AR transcriptional activity. The liquid like properties of the condensates were also validated in-vitro by using recombinant AR protein. Using clinical specimens, we detected the interaction between AR and MED1 in advanced forms of prostate cancer and not in benign tissues. These results suggest that enhanced compartmentalization of AR and coactivators at SEs may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options for PCa by targeting downstream events of androgen activation and DNA binding of AR.

Citation Format: Fan Zhang, Maitree Biswas, Joseph Lee, Shreyas Lingadahalli, Samantha Wong, Christopher Wells, Neetu Saxena, Bei Sun, Ana K. Parra-Nuñez, Christophe Sanchez, Jane Foo, Novia Chan, Lauren Ung, Nabeel Khan, Umut Berkay Altıntaş, Jennifer M. Bui, Yuzhuo Wang, Ladan Fazli, Paul S. Rennie, Nathan Lack, Artem Cherkasov, Martin E. Gleave, Joerg Gsponer, Nada Lallous. Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5729.

Abstract 5731: Structure-based development of a novel MYC inhibitor for neuroendocrine prostate cancer

Background: MYC oncoproteins are important drivers of human cancers. More specifically, NMYC is amplified and overexpressed in neuroendocrine prostate cancer (NEPC). NMYC elicits its oncogenic effects by forming a heterodimer with MAX. This complex binds to regulating elements and activates the transcription of MYC target genes that play roles in tumor growth and progression. It is well known that members of the MYC family of proteins make compelling targets for cancer treatments. However, to this date, no tangible drug candidates have been developed into the clinics for MYC. In previous studies utilizing a rational computer-aided drug discovery (CADD) approach, we identified VPC-70551 as our most active hit. Subsequent optimization with the VPC-70551 scaffold has led to the identification of a new series of compounds. Using in silico drug screening followed by functional validation, we identified a small molecule inhibitor, VPC-70619, that exhibits higher microsomal stability and is better absorbed and tolerated orally than most MYC inhibitors described in the literature.

Method: We used a transcriptional reporter assay to determine the effect of VPC-70619 on MYC-mediated transcription. To validate on-target effect, we evaluated the effect of VPC-70619 treatment on MYC-positive (LASCPC-01, NCIH660, LNCaP N-MYC, 22Rv1 N-MYC) and MYC-negative (HO15.19) cancer cells. We also evaluated the effect of VPC-70619 on MYC/MAX interaction by PLA and on DNA binding by BLI. The direct binding between recombinant MYC/MAX protein complex and VPC-70619 was evaluated by MST.

Results: VPC-70619 inhibited MYC transcriptional activity in dose dependent manner and the proliferation of MYC-positive cell lines. VPC-70619 did not interfere with MYC/MAX interaction however it blocked the complex interaction with DNA. We confirmed the direct binding of VPC-70619 to the purified MYC/MAX complex by using MST.

Conclusion: This project presents the identification of a new MYC inhibitor that blocks the transcriptional activity of this oncogene and elucidates the molecular mechanism of action of this inhibitor. Our findings help prelude the development and discovery of more effective treatments for NEPC patients.

Citation Format: Jane Foo, Anh-Tien Ton, Kriti Singh, Fuqiang Ban, Helene Morin, Joseph Lee, Eric LeBlanc, Nada Lallous, Artem Cherkasov. Structure-based development of a novel MYC inhibitor for neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5731.

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.

DD-GUI: a graphical user interface for deep learning-accelerated virtual screening of large chemical libraries (Deep Docking)

SUMMARY

Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project.

AVAILABILITY AND IMPLEMENTATION

DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

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.

Development of 2-(5,6,7-Trifluoro-1H-Indol-3-yl)-quinoline-5-carboxamide as a Potent, Selective, and Orally Available Inhibitor of Human Androgen Receptor Targeting Its Binding Function-3 for the Treatment of Castration-Resistant Prostate Cancer

Prostate cancer (PCa) patients undergoing androgen deprivation therapy almost invariably develop castration-resistant prostate cancer (CRPC). Targeting the androgen receptor (AR) Binding Function-3 (BF3) site offers a promising option to treat CRPC. However, BF3 inhibitors have been limited by poor potency or inadequate metabolic stability. Through extensive medicinal chemistry, molecular modeling, and biochemistry, we identified 2-(5,6,7-trifluoro-1H-Indol-3-yl)-quinoline-5-carboxamide (VPC-13789), a potent AR BF3 antagonist with markedly improved pharmacokinetic properties. We demonstrate that VPC-13789 suppresses AR-mediated transcription, chromatin binding, and recruitment of coregulatory proteins. This novel AR antagonist selectively reduces the growth of both androgen-dependent and enzalutamide-resistant PCa cell lines. Having demonstrated in vitro efficacy, we developed an orally bioavailable prodrug that reduced PSA production and tumor volume in animal models of CRPC with no observed toxicity. VPC-13789 is a potent, selective, and orally bioavailable antiandrogen with a distinct mode of action that has a potential as novel CRPC therapeutics.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug-gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.

Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns

The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.

A critical overview of computational approaches employed for COVID-19 drug discovery

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

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.

Optimization of New Catalytic Topoisomerase II Inhibitors as an Anti-Cancer Therapy

Simple Summary

DNA topoisomerase II (TOP2) is a drug target for many types of cancers. However, clinically used TOP2 inhibitors not only kill cancer cells, but also damage normal cells, and can even give rise to other types of cancers. To discover new TOP2 inhibitors to more effectively treat cancer patients, we have applied computer-aided drug design technology to develop several TOP2 inhibitors that can strongly inhibit cancer cell growth but exert low side effects. Results of one exemplary compound are presented in this study. It shows several promising drug-like properties that can be potentially developed into anticancer drugs.

Abstract

Clinically used topoisomerase II (TOP2) inhibitors are poison inhibitors that induce DNA damage to cause cancer cell death. However, they can also destroy benign cells and thereby show serious side effects, including cardiotoxicity and drug-induced secondary malignancy. New TOP2 inhibitors with a different mechanism of action (MOA), such as catalytic TOP2 inhibitors, are needed to more effectively control tumor growth. We have applied computer-aided drug design to develop a new group of small molecule inhibitors that are derivatives of our previously identified lead compound T60. Particularly, the compound T638 has shown improved solubility and microsomal stability. It is a catalytic TOP2 inhibitor that potently suppresses TOP2 activity. T638 has a novel MOA by which it binds TOP2 proteins and blocks TOP2–DNA interaction. T638 strongly inhibits cancer cell growth, but exhibits limited genotoxicity to cells. These results indicate that T638 is a promising drug candidate that warrants further development into clinically used anticancer drugs.

Development of an Androgen Receptor Inhibitor Targeting the N-Terminal Domain of Androgen Receptor for Treatment of Castration Resistant Prostate Cancer

Prostate cancer patients undergoing androgen deprivation therapy almost invariably develop castration-resistant prostate cancer. Resistance can occur when mutations in the androgen receptor (AR) render anti-androgen drugs ineffective or through the expression of constitutively active splice variants lacking the androgen binding domain entirely (e.g., ARV7). In this study, we are reporting the discovery of a novel AR-NTD covalent inhibitor 1-chloro-3-[(5-([(2S)-3-chloro-2-hydroxypropyl]amino)naphthalen-1-yl)amino]propan-2-ol (VPC-220010) targeting the AR-N-terminal Domain (AR-NTD). VPC-220010 inhibits AR-mediated transcription of full length and truncated variant ARV7, downregulates AR response genes, and selectively reduces the growth of both full-length AR- and truncated AR-dependent prostate cancer cell lines. We show that VPC-220010 disrupts interactions between AR and known coactivators and coregulatory proteins, such as CHD4, FOXA1, ZMIZ1, and several SWI/SNF complex proteins. Taken together, our data suggest that VPC-220010 is a promising small molecule that can be further optimized into effective AR-NTD inhibitor for the treatment of CRPC.

Evaluation of Darolutamide (ODM201) Efficiency on Androgen Receptor Mutants Reported to Date in Prostate Cancer Patients

Resistance to drug treatments is common in prostate cancer (PCa), and the gain-of-function mutations in human androgen receptor (AR) represent one of the most dominant drivers of progression to resistance to AR pathway inhibitors (ARPI). Previously, we evaluated the in vitro response of 24 AR mutations, identified in men with castration-resistant PCa, to five AR antagonists. In the current work, we evaluated 44 additional PCa-associated AR mutants, reported in the literature, and thus expanded the study of the effect of darolutamide to a total of 68 AR mutants. Unlike other AR antagonists, we demonstrate that darolutamide exhibits consistent efficiency against all characterized gain-of-function mutations in a full-length AR. Additionally, the response of the AR mutants to clinically used bicalutamide and enzalutamide, as well as to major endogenous steroids (DHT, estradiol, progesterone and hydrocortisone), was also investigated. As genomic profiling of PCa patients becomes increasingly feasible, the developed "AR functional encyclopedia" could provide decision-makers with a tool to guide the treatment choice for PCa patients based on their AR mutation status.

Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics

Poison inhibitors of DNA topoisomerase II (TOP2) are clinically used drugs that cause cancer cell death by inducing DNA damage, which mechanism of action is also associated with serious side effects such as secondary malignancy and cardiotoxicity. In contrast, TOP2 catalytic inhibitors induce limited DNA damage, have low cytotoxicity, and are effective in suppressing cancer cell proliferation. They have been sought after to be prospective anticancer therapies. Herein the discovery of new TOP2 catalytic inhibitors is described. A new druggable pocket of TOP2 protein at its DNA binding domain was used as a docking site to virtually screen ~6 million molecules from the ZINC15 library. The lead compound, T60, was characterized to be a catalytic TOP2 inhibitor that binds TOP2 protein and disrupts TOP2 from interacting with DNA, resulting in no DNA cleavage. It has low cytotoxicity, but strongly inhibits cancer cell proliferation and xenograft growth. T60 also inhibits androgen receptor activity and prostate cancer cell growth. These results indicate that T60 is a promising candidate compound that can be further developed into new anticancer drugs.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.

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.

DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules

MOTIVATION

Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s).

RESULTS

In this study, we successfully combined molecular fingerprint descriptors and gene descriptors (derived from gene ontology terms) to train deep neural networks that predict differential gene regulation endpoints collected in LINCS database. We achieved 10-fold cross-validation RAUC scores of and above 0.80, as well as enrichment factors of >5. We validated our models using an external RNA-Seq dataset generated in-house that described the effect of three potent antiandrogens (with different modes of action) on gene expression in LNCaP prostate cancer cell line. The results of this pilot study demonstrate that deep learning models can effectively synergize molecular and genomic descriptors and can be used to screen for novel drug candidates with the desired effect on gene expression. We anticipate that such models can find a broad use in developing novel cancer therapeutics and can facilitate precision oncology efforts.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interleukin-10 and Small Molecule SHIP1 Allosteric Regulators Trigger Anti-inflammatory Effects through SHIP1/STAT3 Complexes

The anti-inflammatory actions of interleukin-10 (IL10) are thought to be mediated primarily by the STAT3 transcription factor, but pro-inflammatory cytokines such as interleukin-6 (IL6) also act through STAT3. We now report that IL10, but not IL6 signaling, induces formation of a complex between STAT3 and the inositol polyphosphate-5-phosphatase SHIP1 in macrophages. Both SHIP1 and STAT3 translocate to the nucleus in macrophages. Remarkably, sesquiterpenes of the Pelorol family, which we previously described as allosteric activators of SHIP1 phosphatase activity, could induce SHIP1/STAT3 complex formation in cells and mimic the anti-inflammatory action of IL10 in a mouse model of colitis. Using crystallography and docking studies we identified a drug-binding pocket in SHIP1. Our studies reveal new mechanisms of action for both STAT3 and SHIP1 and provide a rationale for use of allosteric SHIP1-activating compounds, which mimic the beneficial anti-inflammatory actions of IL10.

Video Abstract

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Loss of normal interleukin-10 (IL10) function results in inflammatory diseases

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IL10 or SHIP1 agonists induce formation of SHIP1/STAT3 complexes

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SHIP1 Y190 phosphorylation is required for SHIP1/STAT3 complex formation

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SHIP1 agonists mimic IL10 anti-inflammatory action in a mouse model of colitis

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.

The value of antimicrobial peptides in the age of resistance

Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available.

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.

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

Structural analysis of avibactam-mediated activation of the bla and mec divergons in methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) infections cause significant mortality and morbidity globally. MRSA resistance to β-lactam antibiotics is mediated by two divergons that control levels of a β-lactamase, PC1, and a penicillin-binding protein poorly acylated by β-lactam antibiotics, PBP2a. Expression of genes encoding these proteins is controlled by two integral membrane proteins, BlaR1 and MecR1, which both have an extracellular β-lactam-binding sensor domain. Here, we solved the X-ray crystallographic structures of the BlaR1 and MecR1 sensor domains in complex with avibactam, a diazabicyclooctane β-lactamase inhibitor at 1.6-2.0 Å resolution. Additionally, we show that S. aureus SF8300, a clinically relevant strain from the USA300 clone of MRSA, responds to avibactam by up-regulating the expression of the blaZ and pbp2a antibiotic-resistance genes, encoding PC1 and PBP2a, respectively. The BlaR1-avibactam structure of the carbamoyl-enzyme intermediate revealed that avibactam is bound to the active-site serine in two orientations ∼180° to each other. Although a physiological role of the observed alternative pose remains to be validated, our structural results hint at the presence of a secondary sulfate-binding pocket that could be exploited in the design of future inhibitors of BlaR1/MecR1 sensor domains or the structurally similar class D β-lactamases. The MecR1-avibactam structure adopted a singular avibactam orientation similar to one of the two states observed in the BlaR1-avibactam structure. Given avibactam up-regulates expression of blaZ and pbp2a antibiotic resistance genes, we suggest further consideration and research is needed to explore what effects administering β-lactam-avibactam combinations have on treating MRSA infections.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Ivermectin inhibits HSP27 and potentiates efficacy of oncogene targeting in tumor models

HSP27 is highly expressed in, and supports oncogene addiction of, many cancers. HSP27 phosphorylation is a limiting step for activation of this protein and a target for inhibition, but its highly disordered structure challenges rational structure-guided drug discovery. We performed multistep biochemical, structural, and computational experiments to define a spherical 24-monomer complex composed of 12 HSP27 dimers with a phosphorylation pocket flanked by serine residues between their N-terminal domains. Ivermectin directly binds this pocket to inhibit MAPKAP2-mediated HSP27 phosphorylation and depolymerization, thereby blocking HSP27-regulated survival signaling and client-oncoprotein interactions. Ivermectin potentiated activity of anti-androgen receptor and anti-EGFR drugs in prostate and EGFR/HER2-driven tumor models, respectively, identifying a repurposing approach for cotargeting stress-adaptive responses to overcome resistance to inhibitors of oncogenic pathway signaling.

Correction: QSAR without borders

Correction for ‘QSAR without borders’ by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.