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.

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.

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.

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 FDA-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.In recent month, our group has developed a novel deep learning platform – Deep Docking (DD) which enables very fast docking of billions of molecular structures and provides up to 6,000X enrichment on the top-predicted ligands compared to conventional docking workflow (without notable loss of information on potential hits). In the current work we applied DD to entire 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro. The compounds are made publicly available for further characterization and development by scientific community.

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.

We have developed a novel, hybrid QSAR-docking approach (called ‘progressive docking’) that can speed up the process of virtual screening by enhancing it with Deep Learning models trained on-the-go on produced docking scores. The developed method can, therefore, predict docking outcome for yet unprocessed molecular entries and hence to progressively remove unfavorable chemical structures from the remaining docking base. This approach provides 50–100X speed increase for the standard docking procedures while retaining >90

The ETS family of proteins consists of 28 transcription factors, many of which have been implicated in development and progression of a variety of cancers. While one family member, ERG, has been rigorously studied in the context of prostate cancer where it plays a critical role, other ETS factors keep emerging as potential hallmark oncodrivers. In recent years, numerous studies have reported initial discoveries of small molecule inhibitors of ETS proteins and opened novel avenues for ETS-directed cancer therapies. This review summarizes the state of the art data on therapeutic targeting of ETS family members and highlights the corresponding drug discovery strategies.

Drug discovery is an extensive and rigorous process that requires up to 2 billion dollars of investments and more than ten years of research and development to bring a molecule “from bench to a bedside”. While virtual screening can significantly enhance drug discovery workflow, it ultimately lags the current rate of expansion of chemical databases that already incorporate billions of purchasable compounds. This surge of available small molecules presents great opportunities for drug discovery but also demands for faster virtual screening methods and protocols. In order to address this challenge, we herein introduce Deep Docking ( D 2 ) - a novel deep learning-based approach which is suited for docking billions of molecular structures. The developed D 2 -platform utilizes quantitative structure-activity relationship (QSAR) based deep models trained on docking scores of subsets of a large chemical library (Big Base) to approximate the docking outcome for yet unprocessed molecular entries and to remove unfavorable structures in an iterative manner. We applied D 2 to virtually screen 1.36 billion molecules form the ZINC15 library against 12 prominent target proteins, and demonstrated up to 100-fold chemical data reduction and 6,000-fold enrichment for top hits, without notable loss of well-docked entities. The developed D 2 protocol can readily be used in conjunction with any docking program and was made publicly available.

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods. The available NMR-resolved structure of the TOX protein was used to model its DNA-binding HMG-box domain. To investigate the druggability of the corresponding protein–DNA interface on TOX, we performed a pilot virtual screening of 200,000 small molecules using in silico docking and identified ‘hot spots’ for drug-binding on the HMG-box domain. We then performed a large-scale virtual screening of 7.6 million drug-like compounds that were available from the ZINC15 database. As a result, a total of 140 top candidate compounds were selected for subsequent in vitro validation. Of those, 18 small molecules have been characterized as selective TOX inhibitors.

Searchable abstracts of presentations at key conferences in oncology ISSN 2631-4657 (online)

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.

The majority of computational methods for predicting toxicity of chemicals are typically based on nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing "black-box" mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflected by the results of the recent Tox21 competition. In the current study, we capitalize on the power of modern AI to predict Tox21 benchmark data using merely simple 2D drawings of chemicals, without employing any chemical descriptors. In particular, we have processed rather trivial 2D sketches of molecules with a supervised 2D convolutional neural network (2DConvNet) and demonstrated that the modern image recognition technology results in prediction accuracies comparable to the state-of-the-art cheminformatics tools. Furthermore, the performance of the image based 2DConvNet model was comparatively evaluated on an external set of compounds from the Prestwick chemical library and resulted in experimental identification of significant and previously unreported antiandrogen potentials for several well established generic drugs.

Genomic alterations involving translocations of the ETS-related gene ERG occur in approximately half of prostate cancer cases. These alterations result in aberrant, androgen-regulated production of ERG protein variants that directly contribute to disease development and progression. This study describes the discovery and characterization of a new class of small molecule ERG antagonists identified through rational in silico methods. These antagonists are designed to sterically block DNA binding by the ETS domain of ERG and thereby disrupt transcriptional activity. We confirmed the direct binding of a lead compound, VPC-18005, with the ERG-ETS domain using biophysical approaches. We then demonstrated VPC-18005 reduced migration and invasion rates of ERG expressing prostate cancer cells, and reduced metastasis in a zebrafish xenograft model. These results demonstrate proof-of-principal that small molecule targeting of the ERG-ETS domain can suppress transcriptional activity and reverse transformed characteristics of prostate cancers aberrantly expressing ERG. Clinical advancement of the developed small molecule inhibitors may provide new therapeutic agents for use as alternatives to, or in combination with, current therapies for men with ERG-expressing metastatic castration-resistant prostate cancer.

The androgen receptor (AR) is a member of the nuclear receptor superfamily of transcription factors and is central to prostate cancer (PCa) progression. Ligand-activated AR engages androgen response elements (AREs) at androgen-responsive genes to drive the expression of gene batteries involved in cell proliferation and cell fate. Understanding the transcriptional targets of the AR has become critical in apprehending the mechanisms driving treatment-resistant stages of PCa. Although AR transcription regulation has been extensively studied, the signaling networks downstream of AR are incompletely described. Semaphorin 3C (SEMA3C) is a secreted signaling protein with roles in nervous system and cardiac development but can also drive cellular growth and invasive characteristics in multiple cancers including PCa. Despite numerous findings that implicate SEMA3C in cancer progression, regulatory mechanisms governing its expression remain largely unknown. Here we identify and characterize an androgen response element within the SEMA3C locus. Using the AR-positive LNCaP PCa cell line, we show that SEMA3C expression is driven by AR through this element and that AR-mediated expression of SEMA3C is dependent on the transcription factor GATA2. SEMA3C has been shown to promote cellular growth in certain cell types so implicit to our findings is the discovery of direct regulation of a growth factor by AR. We also show that FOXA1 is a negative regulator of SEMA3C. These findings identify SEMA3C as a novel target of AR, GATA2, and FOXA1 and expand our understanding of semaphorin signaling and cancer biology.

Future Science Book SeriesAdvances in Tuberculosis Medicinal Chemistry Inhibition of secreted phosphatases as promising novel drug targets for tuberculosisHoracio Bach, Michael Hsing & Artem CherkasovHoracio Bach, Michael Hsing & Artem CherkasovPublished Online:24 Feb 2016https://doi.org/10.4155/fseb2013.13.41AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail View chapterReferences1. Bach H . Role of kinases and phosphatases in host-pathogen interactions . In : Protein Kinases . Da Silva Xavier G ( Ed. ). Intech , Croatia , Chapter 5 , 99 – 122 ( 2012 ). Crossref, Google Scholar2. Cole ST , Brosch R , Parkhill J et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence . Nature 393 , 537 – 544 ( 1998 ). Crossref, Medline, CAS, Google Scholar3. Bach H , Papavinasasundaram KG , Wong D et al. 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Abstract Prostate cancer (PCa) is the second leading cause of cancer-related deaths in North America. The androgen receptor (AR) is a member of the nuclear receptor superfamily of transcription factors and is heavily implicated in PCa progression. First-line therapies frequently target the AR axis and are initially met with favourable response but restored and aberrant AR signaling fuel disease progression to stages for which treatment is palliative. Transcriptional targets of the AR include genes involved in cell growth and cell fate but are not completely described. The semaphorin family of signaling proteins is a large grouping of cell-surface or secreted signaling proteins that function in neurogenesis and development. The roles of semaphorins in cancer are becoming increasingly evident however mechanistic details surrounding their involvement in cancer are poorly defined. One member of the class 3 semaphorins, SEMA3C, has been shown to confer invasive phenotypes in prostate cancer cells. Using the AR-positive LNCaP cell line we show that SEMA3C is an androgen-responsive gene. Additionally, using RSAT DNA analysis software we identify an androgen response element (ARE) in intron 2 of SEMA3C and show that AR is recruited to this genomic region in an androgen-dependent manner in chromatin immunoprecipitation assays. Furthermore, the isolated ARE binds to the AR-DNA binding domain in gel shift assays and can be transactivated by AR in reporter gene assays. Finally we show that the pioneering factor, GATA2, is necessary for AR-mediated expression of SEMA3C. Collectively, our work identifies SEMA3C as a direct transcriptional target of AR in support of our hypothesis that dysregulated AR signaling drives inadvertent upregulation of SEMA3C and PCa disease progression. Deregulated AR signaling underpins prostate cancer progression and underscores the need to elucidate transcriptional targets of AR. Identification of SEMA3C as a novel target of AR provides the rationale for targeted therapies directed against SEMA3C. Citation Format: Kevin J. Tam, Kush Dalal, Michael Hsing, Chi Wing Cheng, Yan Ting Chiang, Aishwariya Sharma, James W. Peacock, Artem Cherkasov, Yuzhuo Wang, Martin E. Gleave, Paul S. Rennie, Christopher J. Ong. Semaphorin 3C is an androgen receptor-regulated gene. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1834.

Nuclear receptors (NRs), a family of 48 transcriptional factors, have been studied intensively for their roles in cancer development and progression. The presence of distinctive ligand binding sites capable of interacting with small molecules has made NRs attractive targets for developing cancer therapeutics. In particular, a number of drugs have been developed over the years to target human androgenand estrogen receptors for the treatment of prostate cancer and breast cancer. In contrast, orphan nuclear receptors (ONRs), which in many cases lack known biological functions or ligands, are still largely under investigated. This review is a summary on ONRs that have been implicated in prostate and breast cancers, specifically retinoic acid-receptor-related orphan receptors (RORs), liver X receptors (LXRs), chicken ovalbumin upstream promoter transcription factors (COUP-TFs), estrogen related receptors (ERRs), nerve growth factor 1B-like receptors, and ‘‘dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1’’ (DAX1). Discovery and development of small molecules that can bind at various functional sites on these ONRs will help determine their biological functions. In addition, these molecules have the potential to act as prototypes for future drug development. Ultimately, the therapeutic value of targeting the ONRs may go well beyond prostate and breast cancers. 2014 Elsevier Ltd. All rights reserved.

Abstract Prostate cancer (PCa) is one of the leading causes of cancer-related death in men worldwide. The common treatment option for recurring and advanced PCa focuses on inhibiting the androgen receptor (AR). Unfortunately, despite an initial response to this treatment, drug resistance occurs, and the cancer relapses to an incurable, castration-resistant form; thus, there is a pressing need for new therapeutics. Previous research has shown that in up to 50% of all prostate cancer cases, the cause of the disease may be attributed to a common genomic rearrangement, the fusion between TMPRSS2 and ERG (ETS-related gene). ERG is a transcription factor mainly involved in hematopoiesis regulation during embryonic development, and it is not normally expressed in prostate cells in adults. However, its fusion with the TMPRSS2 promoter puts ERG under the regulation of AR, and as a consequence, ERG is one of the most commonly overexpressed genes in PCa. ERG overexpression in prostate epithelium has been shown to induce transformation and promote epithelial-mesenchymal transition (EMT) that gives cancer cells enhanced migratory and invasive characteristics. Currently, there is no approved therapeutic targeting ERG or any other member of the ETS family. While targeting transcription factors has been challenging, our integrated research team is specialized in targeting protein-DNA interaction sites. Using our established computer-aided drug discovery pipeline, we have identified several small molecules that can bind to and inhibit ERG. A total of 133 candidate compounds, pre-selected from the in silico screening of millions of chemical structures, were tested using a luciferase-based transcriptional reporter assay across two cell lines: the TMPRSS2-ERG fusion positive VCaP cell line, and the human prostate epithelial cell line (PNT1B) engineered to constitutively express ERG. In addition to transcriptional inhibition, the most potent compounds inhibited migration of PNT1B-ERG cells as demonstrated by a Real-Time Cell Analyzer. Significantly, both compounds shifted the binding spectra in protein NMR assays, indicating their direct interactions with residues located in the DNA binding domain of the ERG protein. We anticipate that results from this project will lead to the development of new drugs that can be used alternatively or synergistically with current anti-AR therapy to benefit patients with the most deadly forms of prostate cancer. (Supported by a grant from Prostate Cancer Canada) Citation Format: Mani Roshan-Moniri, Michael Hsing, Miriam S. Butler, Desmond Lau, Peter Axerio-Cilies, Paul Yen, Ari Kim, Scott Lien, Marta Mroczek, Dennis Ma, Huifang Li, Yubin Guo, Fuqiang Ban, Fariba Ghaidi, Eric LeBlanc, Lawrence McIntosh, Michael Cox, Artem Cherkasov, Paul S. Rennie. Therapeutic targeting of ETS factor ERG for the treatment of prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1652. doi:10.1158/1538-7445.AM2015-1652

The v-ATPase is a fundamental eukaryotic enzyme that is central to cellular homeostasis. Although its impact on key metabolic regulators such as TORC1 is well documented, our knowledge of mechanisms that regulate v-ATPase activity is limited. Here, we report that the Drosophila transcription factor Mitf is a master regulator of this holoenzyme. Mitf directly controls transcription of all 15 v-ATPase components through M-box cis-sites and this coordinated regulation affects holoenzyme activity in vivo. In addition, through the v-ATPase, Mitf promotes the activity of TORC1, which in turn negatively regulates Mitf. We provide evidence that Mitf, v-ATPase and TORC1 form a negative regulatory loop that maintains each of these important metabolic regulators in relative balance. Interestingly, direct regulation of v-ATPase genes by human MITF also occurs in cells of the melanocytic lineage, showing mechanistic conservation in the regulation of the v-ATPase by MITF family proteins in fly and mammals. Collectively, this evidence points to an ancient module comprising Mitf, v-ATPase and TORC1 that serves as a dynamic modulator of metabolism for cellular homeostasis.