Computer-Aided Discovery of Small Molecules Targeting the RNA Splicing Activity of hnRNP A1 in Castration-Resistant Prostate Cancer.

The heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a versatile RNA-binding protein playing a critical role in alternative pre-mRNA splicing regulation in cancer. Emerging data have implicated hnRNP A1 as a central player in a splicing regulatory circuit involving its direct transcriptional control by c-Myc oncoprotein and the production of the constitutively active ligand-independent alternative splice variant of androgen receptor, AR-V7, which promotes castration-resistant prostate cancer (CRPC). As there is an urgent need for effective CRPC drugs, targeting hnRNP A1 could, therefore, serve a dual purpose of preventing AR-V7 generation as well as reducing c-Myc transcriptional output. Herein, we report compound VPC-80051 as the first small molecule inhibitor of hnRNP A1 splicing activity discovered to date by using a computer-aided drug discovery approach. The inhibitor was developed to target the RNA-binding domain (RBD) of hnRNP A1. Further experimental evaluation demonstrated that VPC-80051 interacts directly with hnRNP A1 RBD and reduces AR-V7 messenger levels in 22Rv1 CRPC cell line. This study lays the groundwork for future structure-based development of more potent and selective small molecule inhibitors of hnRNP A1⁻RNA interactions aimed at altering the production of cancer-specific alternative splice isoforms.

Therapeutic Inhibition of Myc in Cancer. Structural Bases and Computer-Aided Drug Discovery Approaches.

Myc (avian myelocytomatosis viral oncogene homolog) represents one of the most sought after drug targets in cancer. Myc transcription factor is an essential regulator of cell growth, but in most cancers it is overexpressed and associated with treatment-resistance and lethal outcomes. Over 40 years of research and drug development efforts did not yield a clinically useful Myc inhibitor. Drugging the "undruggable" is problematic, as Myc inactivation may negatively impact its physiological functions. Moreover, Myc is a disordered protein that lacks effective binding pockets on its surface. It is well established that the Myc function is dependent on dimerization with its obligate partner, Max (Myc associated factor X), which together form a functional DNA-binding domain to activate genomic targets. Herein, we provide an overview of the knowledge accumulated to date on Myc regulation and function, its critical role in cancer, and summarize various strategies that are employed to tackle Myc-driven malignant transformation. We focus on important structure-function relationships of Myc with its interactome, elaborating structural determinants of Myc-Max dimer formation and DNA recognition exploited for therapeutic inhibition. Chronological development of small-molecule Myc-Max prototype inhibitors and corresponding binding sites are comprehensively reviewed and particular emphasis is placed on modern computational drug design methods. On the outlook, technological advancements may soon provide the so long-awaited Myc-Max clinical candidate.

Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor.

The human androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of prostate cancer (PCa). Many forms of castration-resistant prostate cancer (CRPC) still rely on the AR for survival. Currently used antiandrogens face clinical limitations as drug resistance develops in patients over time since they all target the mutation-prone androgen binding site (ABS), where gain-of-function mutations eventually convert antagonists into agonists. With a significant number of reported distinct mutations located across the ABS, it is imperative to develop a prognostic platform which would equip clinicians with prior knowledge and actionable strategies if cases of previously unreported AR mutations are encountered. The goal of this study is to develop a theoretical approach that can predict such previously unreported AR mutants in response to current treatment options for PCa. The expected drug response by these mutants has been modeled using cheminformatics methodology. The corresponding QSAR pipeline has been created, which extracts key protein-ligand interactions and quantifies them by 4D molecular descriptors. The developed models reported with an accuracy reaching 90% and enable prediction of activation of AR mutants by its native ligand as well as assess whether known antiandrogens will act on them as agonists or antagonists. As a result, a previously uncharacterized mutant, T878G, has been predicted to be activated by the latest antiandrogen enzalutamide, and the corresponding experimental evaluation confirmed this prediction. Overall, the developed cheminformatics pipeline provides useful insights toward understanding the changing genomic landscape of advanced PCa.

Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer.

While Myc is an essential regulator of growth in normal cells, it is also frequently associated with cancer progression, therapy-resistance and lethal outcomes in most human cancers. In prostate cancer (PCa), Myc transcription factors are implicated in the pathogenesis and progression of the full spectrum of PCa, from adenocarcinoma to advanced castration-resistant and neuroendocrine phenotypes. Though a high-value therapeutic target, clinically approved anti-Myc drugs have yet to be discovered. To elicit its oncogenic effects, Myc must form a heterodimer with its partner Max, which together bind DNA and activate transcription of a spectrum of target genes that promote cell growth, proliferation, metabolism, and apoptosis while blocking differentiation. In this study, we identified a binding site on the DNA-binding domain of the structurally ordered Myc-Max complex and employed a computer-aided rational drug discovery approach to identify small molecules that effectively inhibit Myc-Max functionality. A large-scale virtual screening protocol implementing structure-based methodologies was utilized to select a set of top-ranked compounds that were subsequently evaluated experimentally and characterized mechanistically for their ability to inhibit Myc-Max transcriptional activity and subsequent downstream functions, to reduce viability in PCa cell lines, disrupt protein-DNA interactions and to induce apoptosis as their mechanism of action. Among compounds identified that effectively inhibit Myc-Max activity with low to mid-micromolar range potency and no or minimal generic cytotoxicity, VPC-70067, a close analog of the previously identified Myc inhibitor 10058-F4, served as proof-of-concept that our in silico drug discovery strategy performed as expected. Compound VPC-70063, of a chemically different scaffold, was the best performer in a panel of in vitro assays, and the forerunner for future hit-to-lead optimization efforts. These findings lay a foundation for developing more potent, specific and clinically optimized Myc-Max inhibitors that may serve as promising therapeutics, alone or in combination with current anti-cancer treatments, for treatment of specific phenotypes or heterogeneous tumors.

Mechanism of the Nitric Oxide Dioxygenase Reaction of Mycobacterium tuberculosis Hemoglobin N.

Many globins convert •NO to innocuous NO3- through their nitric oxide dioxygenase (NOD) activity. Mycobacterium tuberculosis fights the oxidative and nitrosative stress imposed by its host (the toxic effects of O2•- and •NO species and their OONO- and •NO2 derivatives) through the action of truncated hemoglobin N (trHbN), which catalyzes the NOD reaction with one of the highest rates among globins. The general NOD mechanism comprises the following steps: binding of O2 to the heme, diffusion of •NO into the heme pocket and formation of peroxynitrite (OONO-), isomerization of OONO-, and release of NO3-. Using quantum mechanics/molecular mechanics free-energy calculations, we show that the NOD reaction in trHbN follows a mechanism in which heme-bound OONO- undergoes homolytic cleavage to give FeIV═O2- and the •NO2 radical but that these potentially harmful intermediates are short-lived and caged by the heme pocket residues. In particular, the simulations show that Tyr33(B10) side chain is shielded from FeIV═O2- and •NO2 (and protected from irreversible oxidation and nitration) by forming stable hydrogen bonds with Gln58(E11) side chain and Leu54(E7) backbone. Aromatic residues Phe46(CD1), Phe32(B9), and Tyr33(B10) promote NO3- dissociation via C-H···O bonding and provide stabilizing interactions for the anion along its egress route.

G-DOC: a systems medicine platform for personalized oncology.

Currently, cancer therapy remains limited by a "one-size-fits-all" approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual's underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality "omics" data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient's tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major "omics" types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.