A series of three 1,1-bis(4-hydroxyphenyl)-2-(3-hydroxyphenyl)-ethylene derivatives was prepared and evaluated as potential estrogen receptor imaging agents. The compounds display high binding affinity compared to estradiol, with the 2-iodo and 2-bromo-derivatives expressing higher affinity than the parent 2-nonhalogenated derivative. Evaluation in immature female rats also indicate that the compounds were all full estrogenic agonists with potencies in the same order of activity (I∼Br>H). Computational analysis of the interactions between the ligands and ERα-LBD demonstrated positive contribution of halide to binding properties. In preparation for studies using the radiohalogenated analogs, the corresponding protected 2-(tributylstannyl) derivative was prepared and converted to the corresponding 2-iodo-product.
In the current COVID-19 pandemic, it is critical to understand, as swiftly as possible, how the viral proteins function and how their function might be modulated. The machine learning method Partial Order Optimum Likelihood (POOL) is used to predict binding sites in protein structures from SARS-CoV-2, the virus that causes COVID-19. Using the 3D structure of each protein as input, POOL uses computed electrostatic and chemical properties to predict the amino acids that are biochemically active, including residues in catalytic sites, allosteric sites, and other secondary sites. Docking studies are then performed to predict ligands that bind to each of these predicted sites. For instance, for the x-ray crystal structures of the main protease, POOL predicts two sites: the known catalytic site containing the catalytic dyad His41 and Cys145 and a second nearby site on an adjacent face of the protein surface. The x-ray crystal structure of the SARS-CoV-2 2'-O-ribose RNA methyltransferase (NSP16) protein has been reported in complex with its activating partner NSP10 and with two bound ligands, S-adenosylmethionine (SAM) and b-D-fructopyranose (BDF). POOL predicts three binding sites, including the catalytic SAM-binding site, the BDF binding site on the opposite side, and a third site adjacent to the catalytic / SAM-binding site. Predicted binding ligands (including selected compounds from the ZINC and Enamine databases, Chemical Abstract Service database compounds, and COVID-specific libraries from Enamine and Life Chemicals) are reported for several SARS-CoV-2 proteins. Kinetics assays to test for catalytic activity of the main protease and of 2'-O-ribose RNA methyltransferase in the presence of predicted binding ligands with high scores are underway. Theoretical and experimental methods are aimed at identifying molecules having inhibitory effects on the function of viral proteins. Supported by NSF CHE-2030180.
The COVID‐19 pandemic, caused by the Severe Acute Respiratory Coronavirus 2 (SARS‐CoV‐2) virus, first started in the Wuhan region of Hubei, China, and has quickly spread to 191 countries and territories, infecting more than 86.4 million people, and resulting in 1.87 global deaths as of January 6th. With SARS‐CoV‐2’s genomic sequence and protein structures deciphered and updated rapidly, clinical treatments and vaccine developments have proceeded simultaneously as researchers attempt to learn more about the infecting mechanisms of this virus. Among these attempts, computational drug screening for SARS‐CoV‐2 has potential for: (1) narrowing down billions of chemical compounds into a list of possible high‐affinity ligands for SARS‐CoV‐2 protein targets, (2) providing information about the activities of SARS‐CoV‐2 proteins, (3) offering possible treatments, and (4) assisting in scientific knowledge to fight against future coronavirus infections. In this work, computational ligand screening for SARS‐CoV‐2 is a combination of site prediction using machine learning technology Partial Order Optimum Likelihood (POOL) and molecular docking. Among the techniques deployed, the machine learning technology POOL was developed by us and assists in the drug screening process for SARS‐CoV‐2 by predicting targeted protein sites, including those that are not the obvious catalytic sites, such as exosites, allosteric sites, and other interaction sites. Results will be presented for the SARS‐CoV‐2 main protease, non‐structural protein 1 (Nsp1), non‐structural protein 9 (Nsp9), and non‐structural protein 15 (Nsp15). Compounds are taken from a variety of libraries, including the ZINC and Enamine databases. Protein structures are downloaded from the Protein Data Bank (http://www.rcsb.org). Molecular dynamic structure simulations are used to generate structures for ensemble docking.
The virus SARS‐CoV‐2, the cause of the current COVID‐19 pandemic, is not well understood. It is critical to understand how the viral proteins function and how their function may be modulated. Inhibitors that target these enzymes serve as potential therapeutic interventions against COVID‐19. This work uses artificial intelligence methods developed by us to find sites that other methods may not find and therefore, identify potential exosites, allosteric sites, or other sites of interaction in the structures of viral proteins to serve as new targets for the development of antiviral agents. Large datasets of natural and synthetic compounds are computationally searched for molecules that fit into these alternative sites, and any compounds that fit will be targeted for experimental testing for their ability to inhibit the functions of these viral enzymes. This project uses the unique Partial Order Optimum Likelihood (POOL) machine learning method developed by us to predict multiple types of binding sites in SARS‐CoV‐2 proteins, including catalytic sites, allosteric sites, and other interaction sites. Molecular dynamics simulations are used to generate conformations for ensemble docking. Compounds from large molecular libraries are computationally docked into the predicted sites to identify potentially strong binding ligands. We have identified approximately 10000 potential ligands for more than 50 SARS‐CoV‐2 proteins to date. Candidate ligands to selected SARS‐CoV‐2 proteins are experimentally tested in vitro for binding affinity and the effect of the best‐predicted inhibitors on catalytic activities determined by direct biochemical assays. Compound libraries for the study include selected compounds from the ZINC and Enamine databases; Chemical Abstract Service database compounds and COVID‐specific libraries from Enamine and Life Chemicals.
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