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15 results on '"Glen, R.C."'

1. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

Author

Boby, M.L., Fearon, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Chodera, J.D., Lee, A.A., London, N., Delft, A. von, Delft, F. von, Achdout, H., Aimon, A., Alonzi, D.S., Arbon, R., Aschenbrenner, J.C., Balcomb, B.H., Bar-David, E., Barr, H., Ben-Shmuel, A., Bennett, J., Bilenko, V.A., Borden, B., Boulet, P., Bowman, G.R., Brewitz, L., Brun, J., Bvnbs, S., Calmiano, M., Carbery, A., Carney, D.W., Cattermole, E., Chang, E., Chernyshenko, E., Clyde, A., Coffland, J.E., Cohen, G., Cole, J.C., Contini, A., Cox, L., Croll, T.I., Cvitkovic, M., Jonghe, S. De, Dias, A., Donckers, K., Dotson, D.L., Douangamath, A., Duberstein, S., Dudgeon, T., Dunnett, L.E., Eastman, P., Erez, N., Eyermann, C.J., Fairhead, M., Fate, G., Fedorov, O., Fernandes, R.S., Ferrins, L., Foster, R., Foster, H., Fraisse, L., Gabizon, R., García-Sastre, A., Gawriljuk, V.O., Gehrtz, P., Gileadi, C., Giroud, C., Glass, W.G., Glen, R.C., Glinert, I., Godoy, A.S., Gorichko, M., Gorrie-Stone, T., Griffen, E.J., Haneef, A., Hassell Hart, S, Heer, J., Henry, M., Hill, M., Horrell, S., Huang, Q.Y.J., Huliak, V.D., Hurley, M.F.D., Israely, T., Jajack, A., Jansen, J, Jnoff, E., Jochmans, D., John, T., Kaminow, B., Kang, L., Kantsadi, A.L., Kenny, P.W., Kiappes, J.L., Kinakh, S.O., Kovar, B., Krojer, T., La, V.N.T., Laghnimi-Hahn, S., Lefker, B.A., Levy, H., Lithgo, R.M., Logvinenko, I.G., Lukacik, P., Macdonald, H.B., MacLean, E.M., Makower, L.L., Malla, T.R., Marples, P.G., Matviiuk, T., McCorkindale, W., McGovern, B.L., Melamed, S., Melnykov, K.P., Michurin, O., Miesen, P., Mikolajek, H., Milne, B.F., Minh, D., Morris, A., Morris, G.M., Morwitzer, M.J., Moustakas, D., Mowbray, C.E., Nakamura, A.M., Neto, J.B., Neyts, J., Nguyen, L, Noske, G.D., Oleinikovas, V., Oliva, G., Overheul, G.J., Owen, C.D., Pai, R., Pan, J., Paran, N., Payne, A.M., Perry, B., Pingle, M., Pinjari, J., Politi, B., Powell, A., Pšenák, V., Pulido, I., Puni, R., Rangel, V.L., Reddi, R.N., Rees, P., Reid, S.P., Reid, L., Resnick, E., Ripka, E.G., Robinson, R.P., Rodriguez-Guerra, J., Rosales, R., Rufa, D.A., Saar, K., Saikatendu, K.S., Salah, E., Schaller, D., Scheen, J., Schiffer, C.A., Schofield, C.J., Shafeev, M., Shaikh, A., Shaqra, A.M., Shi, J., Shurrush, K., Singh, S., Sittner, A., Sjö, P., Skyner, R., Smalley, A., Smeets, B., Smilova, M.D., Solmesky, L.J., Spencer, J., Strain-Damerell, C., Swamy, V., Tamir, H., Taylor, J.C., Tennant, R.E., Thompson, W., Thompson, A., Tomásio, S., Tomlinson, C.W.E., Tsurupa, I.S., Tumber, A., Vakonakis, I., Rij, R.P. van, Vangeel, L., Varghese, F.S., Vaschetto, M., Vitner, E.B., Voelz, V., Volkamer, A., Walsh, M.A., Ward, W., Weatherall, C., Weiss, S., White, K.M., Wild, C.F., Witt, K.D., Wittmann, M., Wright, N., Yahalom-Ronen, Y., Yilmaz, N.K., Zaidmann, D., Zhang, I., Zidane, H., Zitzmann, N., Zvornicanin, S.N., Boby, M.L., Fearon, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Chodera, J.D., Lee, A.A., London, N., Delft, A. von, Delft, F. von, Achdout, H., Aimon, A., Alonzi, D.S., Arbon, R., Aschenbrenner, J.C., Balcomb, B.H., Bar-David, E., Barr, H., Ben-Shmuel, A., Bennett, J., Bilenko, V.A., Borden, B., Boulet, P., Bowman, G.R., Brewitz, L., Brun, J., Bvnbs, S., Calmiano, M., Carbery, A., Carney, D.W., Cattermole, E., Chang, E., Chernyshenko, E., Clyde, A., Coffland, J.E., Cohen, G., Cole, J.C., Contini, A., Cox, L., Croll, T.I., Cvitkovic, M., Jonghe, S. De, Dias, A., Donckers, K., Dotson, D.L., Douangamath, A., Duberstein, S., Dudgeon, T., Dunnett, L.E., Eastman, P., Erez, N., Eyermann, C.J., Fairhead, M., Fate, G., Fedorov, O., Fernandes, R.S., Ferrins, L., Foster, R., Foster, H., Fraisse, L., Gabizon, R., García-Sastre, A., Gawriljuk, V.O., Gehrtz, P., Gileadi, C., Giroud, C., Glass, W.G., Glen, R.C., Glinert, I., Godoy, A.S., Gorichko, M., Gorrie-Stone, T., Griffen, E.J., Haneef, A., Hassell Hart, S, Heer, J., Henry, M., Hill, M., Horrell, S., Huang, Q.Y.J., Huliak, V.D., Hurley, M.F.D., Israely, T., Jajack, A., Jansen, J, Jnoff, E., Jochmans, D., John, T., Kaminow, B., Kang, L., Kantsadi, A.L., Kenny, P.W., Kiappes, J.L., Kinakh, S.O., Kovar, B., Krojer, T., La, V.N.T., Laghnimi-Hahn, S., Lefker, B.A., Levy, H., Lithgo, R.M., Logvinenko, I.G., Lukacik, P., Macdonald, H.B., MacLean, E.M., Makower, L.L., Malla, T.R., Marples, P.G., Matviiuk, T., McCorkindale, W., McGovern, B.L., Melamed, S., Melnykov, K.P., Michurin, O., Miesen, P., Mikolajek, H., Milne, B.F., Minh, D., Morris, A., Morris, G.M., Morwitzer, M.J., Moustakas, D., Mowbray, C.E., Nakamura, A.M., Neto, J.B., Neyts, J., Nguyen, L, Noske, G.D., Oleinikovas, V., Oliva, G., Overheul, G.J., Owen, C.D., Pai, R., Pan, J., Paran, N., Payne, A.M., Perry, B., Pingle, M., Pinjari, J., Politi, B., Powell, A., Pšenák, V., Pulido, I., Puni, R., Rangel, V.L., Reddi, R.N., Rees, P., Reid, S.P., Reid, L., Resnick, E., Ripka, E.G., Robinson, R.P., Rodriguez-Guerra, J., Rosales, R., Rufa, D.A., Saar, K., Saikatendu, K.S., Salah, E., Schaller, D., Scheen, J., Schiffer, C.A., Schofield, C.J., Shafeev, M., Shaikh, A., Shaqra, A.M., Shi, J., Shurrush, K., Singh, S., Sittner, A., Sjö, P., Skyner, R., Smalley, A., Smeets, B., Smilova, M.D., Solmesky, L.J., Spencer, J., Strain-Damerell, C., Swamy, V., Tamir, H., Taylor, J.C., Tennant, R.E., Thompson, W., Thompson, A., Tomásio, S., Tomlinson, C.W.E., Tsurupa, I.S., Tumber, A., Vakonakis, I., Rij, R.P. van, Vangeel, L., Varghese, F.S., Vaschetto, M., Vitner, E.B., Voelz, V., Volkamer, A., Walsh, M.A., Ward, W., Weatherall, C., Weiss, S., White, K.M., Wild, C.F., Witt, K.D., Wittmann, M., Wright, N., Yahalom-Ronen, Y., Yilmaz, N.K., Zaidmann, D., Zhang, I., Zidane, H., Zitzmann, N., and Zvornicanin, S.N.

Abstract

Item does not contain fulltext, We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Published
2023

3. Metabolic Fingerprinting Links Oncogenic PIK3CA with Enhanced Arachidonic Acid-Derived Eicosanoids

Author

Koundouros, N., Karali, E., Tripp, A., Valle, A., Inglese, P., Perry, N.J.S., Magee, D.J., Anjomani Virmouni, S., Elder, G.A., Tyson, A.L., Dória, M.L., van Weverwijk, A., Soares, R.F., Isacke, C.M., Nicholson, J.K., Glen, R.C., Takats, Z., Poulogiannis, G., Koundouros, N., Karali, E., Tripp, A., Valle, A., Inglese, P., Perry, N.J.S., Magee, D.J., Anjomani Virmouni, S., Elder, G.A., Tyson, A.L., Dória, M.L., van Weverwijk, A., Soares, R.F., Isacke, C.M., Nicholson, J.K., Glen, R.C., Takats, Z., and Poulogiannis, G.

Abstract

Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction.

Published
2020

5. How do metabolites differ from their parent molecules and how are they excreted?

Author

Kirchmair, J., Howlett, A., Peironcely, J.E., Murrell, D.S., Williamson, M.J., Adams, S.E., Hankemeier, T., Buren, L. van, Duchateau, G., Klaffke, W., and Glen, R.C.

Subjects
Life, Biomedical Innovation, QS - Quality & Safety, EELS - Earth, Environmental and Life Sciences, Biology, Healthy Living
Abstract

Understanding which physicochemical properties, or property distributions, are favorable for successful design and development of drugs, nutritional supplements, cosmetics, and agrochemicals is of great importance. In this study we have analyzed molecules from three distinct chemical spaces (i) approved drugs, (ii) human metabolites, and (iii) traditional Chinese medicine (TCM) to investigate four aspects determining the disposition of small organic molecules. First, we examined the physicochemical properties of these three classes of molecules and identified characteristic features resulting from their distinctive biological functions. For example, human metabolites and TCM molecules can be larger and more hydrophobic than drugs, which makes them less likely to cross membranes. We then quantified the shifts in physicochemical property space induced by metabolism from a holistic perspective by analyzing a data set of several thousand experimentally observed metabolic trees. Results show how the metabolic system aims to retain nutrients/micronutrients while facilitating a rapid elimination of xenobiotics. In the third part we compared these global shifts with the contributions made by individual metabolic reactions. For better resolution, all reactions were classified into phase I and phase II biotransformations. Interestingly, not all metabolic reactions lead to more hydrophilic molecules. We were able to identify biotransformations leading to an increase of logP by more than one log unit, which could be used for the design of drugs with enhanced efficacy. The study closes with the analysis of the physicochemical properties of metabolites found in the bile, faeces, and urine. Metabolites in the bile can be large and are often negatively charged. Molecules with molecular weight >500 Da are rarely found in the urine, and most of these large molecules are charged phase II conjugates. © 2013 American Chemical Society.

Published
2013

6. An XML infrastructure for eScience

Author

Zhang, Y., Murray-Rust, P., Dove, M., Glen, R.C., Rzepa, H.S., Townsend, J.A., Tyrell, S., Wakelin, J., and Willighagen, E.L.

Subjects
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Analytical Chemistry
Abstract

Contains fulltext : 58099.pdf (Publisher’s version ) (Closed access) Proceedings of UK e-Science All Hands Meeting 2004

Published
2004

11. Molecular Flexibility of N-Acyl Heterocycles Studied Using 1 3C NMR Spectroscopy and Computational Chemistry

Author

Salman, S.R., Farrant, R.D., Glen, R.C., and Lindon, J.C.

Abstract

AbstractThe 13C NMR spectra of a series of N-acyl six-member ring nitrogen heterocycles have been measured as a function of temperature and the results interpreted in terms of restricted amide rotation and ring inversion. Slow rotation is observed on the NMR time scale around the N-CO bond whilst the ring inversion is fast at ambient temperature. High temperature experiments have been undertaken to investigate the free energy of activation for the amide rotation and low temperature NMR experiments were used to probe the ring inversion process. The experimental results have been compared with theoretical calculations using molecular orbital, molecular mechanics and molecular dynamics approaches and good agreement is observed in all cases.

Published
1998
Full Text
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15. Improved robustness in time series analysis of gene expression data by polynomial model based clustering

Author

Christine A. Orengo, Nigel Martin, Allan Tucker, Xiaohui Liu, Stephen Swift, Michael Hirsch, Paul Kellam, Berthold, M.R., Glen, R.C., Fischer, I., Berthold, MR, Glen, R, and Fischer, I

Subjects
Clustering high-dimensional data, Fuzzy clustering, Data stream clustering, csis, CURE data clustering algorithm, Computer science, Correlation clustering, Canopy clustering algorithm, Data mining, Missing data, computer.software_genre, Cluster analysis, computer
Abstract

Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data. This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using these models to cluster the data. Similarity measures for polynomials are given that comply with commonly used standard measures. The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It shows significantly better results as noise and missing data are increased.

Published
2006

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