Search

Your search keyword '"Uko Maran"' showing total 141 results
Start Over Author "Uko Maran"
141 results on '"Uko Maran"'

2. ELIXIR and Toxicology: a community in development [version 2; peer review: 2 approved]

Author

Evan E. Bolton, Haralambos Sarimveis, Egon L. Willighagen, Hervé Ménager, Susanna-Assunta Sansone, Nina Jeliazkova, Antony J. Williams, Sirarat Sarntivijai, Emma L. Schymanski, Rob Stierum, Karel Berka, Pascal Kahlem, Jos Bessems, Pavel Babica, Montserrat Cases, Ludek Blaha, Hristo Aladjov, Reza Aalizadeh, Karine Audouze, Kasia Arturi, Alasdair Gray, Roland Grafström, Daan P. Geerke, Henner Hollert, Ola Spjuth, John M. Hancock, Kirtan Dave, Dimitrios Ε. Damalas, Thomas Exner, Marco Dilger, Boï Kone, Todor Kondic, Uko Maran, Steffen Neumann, Iseult Lynch, Fabien Jourdan, Philippe Rocca-Serra, Ferran Sanz, Danyel Jennen, Jos Kleinjans, Jana Klanova, Reza M. Salek, Sylvie Remy, Tobias Schulze, Brett Sallach, Penny Nymark, Sergio Martinez Cuesta, Noelia Ramirez, Herbert Oberacher, Craig E. Wheelock, Gerard J.P. van Westen, Barbara Zdrazil, Hilda Witters, Jonathan Tedds, Marvin Martens, Jaroslav Slobodnik, Ralf J.M. Weber, Nikolaos Thomaidis, Anže Županič, and Chris T. Evelo

Subjects
Toxicology, ELIXIR, interoperability, FAIR, eng, Medicine, Science
Abstract

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Published
2023
Full Text
View/download PDF

3. Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances

Author

Mare Oja, Sulev Sild, Geven Piir, and Uko Maran

Subjects
solubility, drug substances, QSAR, QSPR, fit-for-purpose training set, multiple linear regression, Pharmacy and materia medica, RS1-441
Abstract

Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors’ systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure–property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.

Published
2022
Full Text
View/download PDF

4. Natural Variation in Arabidopsis Cvi-0 Accession Reveals an Important Role of MPK12 in Guard Cell CO2 Signaling.

Author

Liina Jakobson, Lauri Vaahtera, Kadri Tõldsepp, Maris Nuhkat, Cun Wang, Yuh-Shuh Wang, Hanna Hõrak, Ervin Valk, Priit Pechter, Yana Sindarovska, Jing Tang, Chuanlei Xiao, Yang Xu, Ulvi Gerst Talas, Alfonso T García-Sosa, Saijaliisa Kangasjärvi, Uko Maran, Maido Remm, M Rob G Roelfsema, Honghong Hu, Jaakko Kangasjärvi, Mart Loog, Julian I Schroeder, Hannes Kollist, and Mikael Brosché

Subjects
Biology (General), QH301-705.5
Abstract

Plant gas exchange is regulated by guard cells that form stomatal pores. Stomatal adjustments are crucial for plant survival; they regulate uptake of CO2 for photosynthesis, loss of water, and entrance of air pollutants such as ozone. We mapped ozone hypersensitivity, more open stomata, and stomatal CO2-insensitivity phenotypes of the Arabidopsis thaliana accession Cvi-0 to a single amino acid substitution in MITOGEN-ACTIVATED PROTEIN (MAP) KINASE 12 (MPK12). In parallel, we showed that stomatal CO2-insensitivity phenotypes of a mutant cis (CO2-insensitive) were caused by a deletion of MPK12. Lack of MPK12 impaired bicarbonate-induced activation of S-type anion channels. We demonstrated that MPK12 interacted with the protein kinase HIGH LEAF TEMPERATURE 1 (HT1)-a central node in guard cell CO2 signaling-and that MPK12 functions as an inhibitor of HT1. These data provide a new function for plant MPKs as protein kinase inhibitors and suggest a mechanism through which guard cell CO2 signaling controls plant water management.

Published
2016
Full Text
View/download PDF

5. In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids

Author

Birgit Viira, Thibault Gendron, Don Antoine Lanfranchi, Sandrine Cojean, Dragos Horvath, Gilles Marcou, Alexandre Varnek, Louis Maes, Uko Maran, Philippe M. Loiseau, and Elisabeth Davioud-Charvet

Subjects
antimalarial, quantitative structure-activity relationships (QSAR), curcuminoid, Michael addition, Plasmodium falciparum, thioredoxin reductase, in silico, Organic chemistry, QD241-441
Abstract

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.

Published
2016
Full Text
View/download PDF

6. ELIXIR and Toxicology: a community in development [version 2; peer review: 2 approved]

Author

Marvin Martens, Rob Stierum, Emma L. Schymanski, Chris T. Evelo, Reza Aalizadeh, Hristo Aladjov, Kasia Arturi, Karine Audouze, Pavel Babica, Karel Berka, Jos Bessems, Ludek Blaha, Evan E. Bolton, Montserrat Cases, Dimitrios Ε. Damalas, Kirtan Dave, Marco Dilger, Thomas Exner, Daan P. Geerke, Roland Grafström, Alasdair Gray, John M. Hancock, Henner Hollert, Nina Jeliazkova, Danyel Jennen, Fabien Jourdan, Pascal Kahlem, Jana Klanova, Jos Kleinjans, Todor Kondic, Boï Kone, Iseult Lynch, Uko Maran, Sergio Martinez Cuesta, Hervé Ménager, Steffen Neumann, Penny Nymark, Herbert Oberacher, Noelia Ramirez, Sylvie Remy, Philippe Rocca-Serra, Reza M. Salek, Brett Sallach, Susanna-Assunta Sansone, Ferran Sanz, Haralambos Sarimveis, Sirarat Sarntivijai, Tobias Schulze, Jaroslav Slobodnik, Ola Spjuth, Jonathan Tedds, Nikolaos Thomaidis, Ralf J.M. Weber, Gerard J.P. van Westen, Craig E. Wheelock, Antony J. Williams, Hilda Witters, Barbara Zdrazil, Anže Županič, and Egon L. Willighagen

Subjects
Opinion Article, Articles, Toxicology, ELIXIR, interoperability, FAIR
Abstract

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Published
2023
Full Text
View/download PDF

7. ELIXIR and Toxicology: a community in development [version 1; peer review: 1 approved, 1 approved with reservations]

Author

Marvin Martens, Rob Stierum, Emma L. Schymanski, Chris T. Evelo, Reza Aalizadeh, Hristo Aladjov, Kasia Arturi, Karine Audouze, Pavel Babica, Karel Berka, Jos Bessems, Ludek Blaha, Evan E. Bolton, Montserrat Cases, Dimitrios Ε. Damalas, Kirtan Dave, Marco Dilger, Thomas Exner, Daan P. Geerke, Roland Grafström, Alasdair Gray, John M. Hancock, Henner Hollert, Nina Jeliazkova, Danyel Jennen, Fabien Jourdan, Pascal Kahlem, Jana Klanova, Jos Kleinjans, Todor Kondic, Boï Kone, Iseult Lynch, Uko Maran, Sergio Martinez Cuesta, Hervé Ménager, Steffen Neumann, Penny Nymark, Herbert Oberacher, Noelia Ramirez, Sylvie Remy, Philippe Rocca-Serra, Reza M. Salek, Brett Sallach, Susanna-Assunta Sansone, Ferran Sanz, Haralambos Sarimveis, Sirarat Sarntivijai, Tobias Schulze, Jaroslav Slobodnik, Ola Spjuth, Jonathan Tedds, Nikolaos Thomaidis, Ralf J.M. Weber, Gerard J.P. van Westen, Craig E. Wheelock, Antony J. Williams, Hilda Witters, Barbara Zdrazil, Anže Županič, and Egon L. Willighagen

Subjects
Opinion Article, Articles, Toxicology, ELIXIR, interoperability, FAIR
Abstract

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Published
2021
Full Text
View/download PDF

10. Synthesis of 6′-galactosyllactose, a deviant human milk oligosaccharide, with the aid of Candida antarctica lipase-B

Author

Kaarel Erik Hunt, Alfonso T. García-Sosa, Tatsiana Shalima, Uko Maran, Raivo Vilu, and Tõnis Kanger

Subjects
Organic Chemistry, Physical and Theoretical Chemistry, Biochemistry
Abstract

Using Novozyme N435 in organic media led to selective deacetylation of various pyranose saccharides. Two of the products were then used to synthesise 6'-galactosyllactose in overall a short pathway.

Published
2022
Full Text
View/download PDF

22. Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives

Author

Uko Maran and S. Zukić

Subjects
Models, Molecular, Drug, Quantitative structure–activity relationship, media_common.quotation_subject, Quantitative Structure-Activity Relationship, Antineoplastic Agents, Bioengineering, 01 natural sciences, HeLa, chemistry.chemical_compound, Drug Discovery, Cervical carcinoma, medicine, Humans, media_common, Xanthene, biology, 010405 organic chemistry, Cancer, General Medicine, medicine.disease, biology.organism_classification, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, Xanthenes, chemistry, Cancer research, Molecular Medicine, Female, HeLa Cells
Abstract

Cancer remains one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives have been synthesized in one of our laboratories and showed anti-proliferative activity in HeLa cancer cell lines. This series serves as a good basis to develop quantitative structure-activity relationship (QSAR), to study the relations between anti-proliferative activity and chemical structures. A QSAR model has been derived that relies only on two-dimensional molecular descriptors, providing mechanistic insight into the anti-proliferative activity of xanthene derivatives. The model is validated internally and externally and additionally with the set of inactive compounds of the original data, confirming model applicability for the design and discovery of novel xanthene derivatives. The QSAR model is available at the QsarDB repository (http://dx.doi.10.15152/QDB.237).

Published
2020
Full Text
View/download PDF

23. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors

Author

Uko Maran, Meelis Käärik, Jaan Leis, Mati Arulepp, and Maike Käärik

Subjects
Materials science, Double-layer capacitance, chemistry.chemical_element, 02 engineering and technology, General Chemistry, Electrolyte, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, Capacitance, Reference electrode, 0104 chemical sciences, Chemical engineering, chemistry, Specific surface area, Gravimetric analysis, General Materials Science, 0210 nano-technology, Porosity, Carbon
Abstract

The development of nanoporous carbon-based energy storage is a fast-growing area. To assist these developments, it is necessary to establish simple criteria and relationships between electric double-layer (EDL) capacitance and the nature of porous carbon used as an electrode material. Under special attention is carbide-derived carbon (CDC) due to high content of micropores and well tunable pore size distribution. In the current study, experimentally determined structure descriptors were compiled for 110 CDC materials, and the Quantitative nano-Structure-Property Relationship (QnSPR) approach was used for the statistical analysis and modelling of the EDL capacitance. Experimentally determined structure descriptors – the variable numeric porosity characteristics of CDC materials, were determined from N2 and CO2 adsorption measurements. Electrochemical characterization of CDC based electrodes was performed in 3-electrode test-cells using carbon reference electrode and 1.5 M spiro-(1,1′)-bipyrrolidinium tetrafluoroborate (SBP–BF4) in acetonitrile as the electrolyte. It was shown that combining experimentally derived molecular descriptors of porosity, like specific surface area and volume-fractions of pore size distribution, calculated by density functional theory, allows accurate prediction of EDL capacitance. The QnSPR-s describing the gravimetric (R2 = 0.91) and the volumetric cathodic capacitances (R2 = 0.95) were developed for the nanoporous carbon in SBP–BF4 electrolyte.

Published
2020
Full Text
View/download PDF

41. ELIXIR and Toxicology : a community in development

Author

Noelia Ramirez, Iseult Lynch, Nikolaos S. Thomaidis, Ralf J. M. Weber, Hilda Witters, Evan E Bolton, Antony Williams, Pavel Babica, Susanna-Assunta Sansone, Kasia Arturi, Anže Županič, Gerard J.P. van Westen, Karine Audouze, Sergio Martinez Cuesta, Tobias Schulze, Jos Bessems, Dimitrios Damalas, Montserrat Cases, Penny Nymark, Ferran Sanz, Uko Maran, Haralambos Sarimveis, Ludek Blaha, Jaroslav Slobodnik, Jos C. S. Kleinjans, Todor Kondic, Hristo Aladjov, Egon Willighagen, Hervé Ménager, Brett Sallach, Danyel Jennen, Sirarat Sarntivijai, Roland C. Grafström, Rob Stierum, Jonathan Tedds, John M. Hancock, Reza M. Salek, Boï Kone, Karel Berka, Herbert Oberacher, Craig E. Wheelock, Steffen Neumann, Alasdair J. G. Gray, Pascal Kahlem, Sylvie Remy, Emma L. Schymanski, Marco Dilger, Ola Spjuth, Barbara Zdrazil, Marvin Martens, Kirtan Dave, Jana Klánová, Henner Hollert, Daan P. Geerke, Philippe Rocca-Serra, Nina Jeliazkova, Thomas Exner, Chris T. Evelo, Fabien Jourdan, Reza Aalizadeh, Department of Bioinformatics, Maastricht University Medical Center (BiGCaT), The Netherlands Organisation for Applied Scientific Research (TNO), Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg [Luxembourg], Maastricht Centre for Systems Biology, Maastricht University Medical Center (MaCSBio), National and Kapodistrian University of Athens (NKUA), Bulgarian Academy of Sciences (BAS), Swiss Federal Institute of Aquatic Science & Technology (EAWA), Toxicité environnementale, cibles thérapeutiques, signalisation cellulaire (T3S - UMR_S 1124), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Research Centre for Toxic Compounds in the Environment [Brno] (RECETOX / MUNI), Faculty of Science [Brno] (SCI / MUNI), Masaryk University [Brno] (MUNI)-Masaryk University [Brno] (MUNI), Palacky University Olomouc, Flemish Institute for Technological Research (VITO), National Center for Biotechnology Information (NCBI), Chemotargets SL, GSFC University, Forschungs- und Beratungsinstitut Gefahrstoffe GmbH (FoBiG), Seven Past Nine, Vrije Universiteit Amsterdam [Amsterdam] (VU), Karolinska Institutet [Stockholm], Misvik Biology, Heriot-Watt University [Edinburgh] (HWU), ELIXIR Hub [Cambridge], Goethe-University Frankfurt am Main, Ideaconsult, Maastricht University [Maastricht], ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Faculté de Médecine, de pharmacie et d’Odonto-Stomatologie [Bamako, Mali] (FMPOS), Université de Bamako, University of Birmingham [Birmingham], University of Tartu, AstraZeneca [Cambridge, UK], Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Institut Français de Bioinformatique (IFB-CORE), Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Leibniz Institute of Plant Biochemistry (IPB), Innsbruck Medical University = Medizinische Universität Innsbruck (IMU), Universitat Rovira i Virgili, University of Oxford, Centre International de Recherche contre le Cancer - International Agency for Research on Cancer (CIRC - IARC), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), University of York [York, UK], Universitat Pompeu Fabra [Barcelona] (UPF), National Technical University of Athens [Athens] (NTUA), Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Environmental Institute Kos, Uppsala University, Leiden Academic Center for Drug Research, United States Environmental Protection Agency [Cincinnati], University of Vienna [Vienna], National Institute of Biology [Ljubljana] (NIB), This work received funding from the European Union’s Horizon 2020 research infrastrcuture programme via the OpenRiskNet project under grant agreement No. 731075.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 681002 (EU-ToxRisk).This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 814572.SN acknowledges BMBF funding under grant number 031L0107.This work was supported by OBERON (https://oberon-4eu.com), a project funded by the European Union's Horizon 2020 research and innovation program under the grant agreement No. 825712.This work was supported by the European Union,'s Horizon 2020 research and innovation program HBM4EU, grant agreement No. 733032 (https://www.hbm4eu.eu).Supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 859891.Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.This work was supported by the European Union,'s Horizon 2020 research and innovation program HARMLESS, grant agreement No. 953183.This work was supported by the European Union,'s Horizon 2020 research and innovation program Gov4Nano, grant agreement No. 814401.This work was supported by the Swedish Fund for Research Without Animal Experiments under grant number N2020-0005.This work was supported by the European Union,'s Horizon 2020 research and innovation program NTS-EXPOSURE, Grant agreement ID: 896141.This work was supported by the European Union,'s LIFE program LIFE-APEX, Grant agreement ID: LIFE17 ENV/SK/000355.This study was funded by the German Environment Agency within the PHION project (grant number 3718 674150).The Data Readiness Group is supported, in this ELIXIR Community, by the H2020 Precision Toxicology project (H2020-EU 965406).The Data Readiness Group is supported, in this ELIXIR Community, by the Wellcome ISA-InterMine project (208381/A/17/Z).The Data Readiness Group is supported, in this ELIXIR Community, by the Wellcome FAIRsharing project (212930/Z/18/Z).This work received funding from the European Union’s Horizon 2020 research and innovation programme RiskGONE Project under grant agreement No. 814425.NR's research is funded by a Miguel Servet contract (CO19/00060) from Instituto de Salud Carlos III, cofinanced by the European Union.UM (Uni. of Tartu) is grateful for support to Ministry of Education and Research, Republic of Estonia through Estonian Research Council (grant number IUT34-14) and to European Union European Regional Development Fund through Foundation Archimedes (grant number TK143, Centre of Excellence in Molecular Cell Engineering).Development and Implementation of a Sustainable Modelling Platform for NanoInformatics.Linking LRI Ambit chemoinformatic system with the IUCLID substance database to support read-across of substance endpoint data and category formation.This work was supported by the French Ministry of Research and National Research Agency as part of the French MetaboHUB, the national metabolomics and fluxomics infrastructure (Grant ANR-INBS-0010).This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement GOLIATH No. 825489., ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), European Project: 681002,H2020,H2020-PHC-2015-single-stage_RTD,EU-ToxRisk(2016), European Project: 825712,H2020-EU.3.1.1. - Understanding health, wellbeing and disease,H2020-SC1-2018-Single-Stage-RTD,OBERON(2019), European Project: 733032,H2020,HBM4EU(2017), European Project: 814425,RiskGONE, European Project: 825489,H2020,GOLIATH(2019), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ménager, Hervé, Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - - METABOHUB2011 - ANR-11-INBS-0010 - INBS - VALID, An Integrated European ‘Flagship’ Program Driving Mechanism-based Toxicity Testing and Risk Assessment for the 21st Century - EU-ToxRisk - - H20202016-01-01 - 2021-12-31 - 681002 - VALID, An integrative strategy of testing systems for identification of EDs related to metabolic disorders - OBERON - - H2020-EU.3.1.1. - Understanding health, wellbeing and disease2019-01-01 - 2019-12-31 - 825712 - VALID, European Human Biomonitoring Initiative - HBM4EU - - H20202017-01-01 - 2021-12-31 - 733032 - VALID, Risk Governance of Nanotechnology - RiskGONE - 814425 - INCOMING, and Beating Goliath: Generation Of NoveL, Integrated and Internationally Harmonised Approaches for Testing Metabolism Disrupting Compounds - GOLIATH - - H20202019-01-19 - 2023-12-31 - 825489 - VALID

Subjects
Interoperability, Context (language use), interoperability, Predictive toxicology, Multidisciplinary, general & others [F99] [Life sciences], Pharmacology and Toxicology, 010501 environmental sciences, Toxicology, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], General Pharmacology, Toxicology and Pharmaceutics, Chemical risk, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology, 0105 earth and related environmental sciences, computer.programming_language, FAIR, 0303 health sciences, Government, General Immunology and Microbiology, General Medicine, Farmakologi och toxikologi, Sketch, 3. Good health, ELIXIR, [SDV.TOX] Life Sciences [q-bio]/Toxicology, [SDV.TOX]Life Sciences [q-bio]/Toxicology, Elixir (programming language), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer
Abstract

International audience; Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Published
2021
Full Text
View/download PDF

42. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling

Author

Uko Maran and Mare Oja

Subjects
0301 basic medicine, Quantitative structure–activity relationship, Cell Membrane Permeability, Membrane permeability, Drug Compounding, Synthetic membrane, Administration, Oral, Quantitative Structure-Activity Relationship, Pharmaceutical Science, Models, Biological, Permeability, Intestinal absorption, 03 medical and health sciences, Molecular descriptor, Humans, Chromatography, Chemistry, Reproducibility of Results, Experimental data, Membranes, Artificial, Hydrogen-Ion Concentration, Permeability (earth sciences), 030104 developmental biology, Membrane, Intestinal Absorption, Pharmaceutical Preparations
Abstract

The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9). The presented data are in good agreement with human intestinal absorption, showing a clear influence of pH on the efficiency of intestinal absorption. For the measured data, simple and general quantitative structure-activity relationships (QSARs) were developed for each pH that makes it possible to predict the pH profiles for passive membrane permeability (i.e., a pH-permeability profile), and these predictions coincide well with the experimental data. QSARs are also proposed for the data series of highest and intrinsic membrane permeability. The molecular descriptors in the models were analysed and mechanistically related to the interaction pattern of permeability in membranes. In addition to the regression models, classification models are also proposed. All models were successfully validated and blind tested with external data. The models are available in the QsarDB repository (http://dx.doi.org/10.15152/QDB.203).

Published
2018
Full Text
View/download PDF

43. Quantitative Nano-Structure–Property Relationships for the Nanoporous Carbon: Predicting the Performance of Energy Storage Materials

Author

Jaan Leis, Maike Käärik, Uko Maran, Mati Arulepp, and Anti Perkson

Subjects
Materials science, Nanoporous, Energy Engineering and Power Technology, chemistry.chemical_element, Nanotechnology, 02 engineering and technology, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, Capacitance, Energy storage, 0104 chemical sciences, Amorphous solid, chemistry, Electrode, Nano, Materials Chemistry, Electrochemistry, Chemical Engineering (miscellaneous), Texture (crystalline), Electrical and Electronic Engineering, 0210 nano-technology, Carbon
Abstract

Nanoporous carbon-based energy storage is a fast-growing research field thanks to high energy densities of carbon electrodes with nanoporous amorphous texture. To support the developments on electr...

Published
2018
Full Text
View/download PDF

44. The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids

Author

Karl Marti Toots, William E. Acree, Jaan Leis, Uko Maran, and Sulev Sild

Subjects
Hydrogen bond, Thermodynamics, Electrolyte, Condensed Matter Physics, London dispersion force, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Ion, Partition coefficient, chemistry.chemical_compound, chemistry, Molecular descriptor, Ionic liquid, Materials Chemistry, Partition (number theory), Physical and Theoretical Chemistry, Spectroscopy
Abstract

Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning (ML) approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for selecting both ionic liquids and organic compounds as quickly, cost-effectively, and as accurately as possible. Therefore, multiple linear regression (MLR) and random forest (RF) quantitative structure–property relationships (QSPRs) were developed for predicting the gas-ionic liquid partition coefficient (log K) of structurally variable organic solutes in the ionic liquids N-butyl-N-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([BMPyrr]+[FAP]−), N-butyl-N-methylpyrrolidinium tricyanomethanide ([BMPyrr]+[C(CN)3]−) and 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([MeoeMPyrr]+[FAP]−). All derived models have excellent prediction capability evidenced by high 5-fold cross-validated coefficients of determination in the range 0.88 – 0.94, complemented with other high statistical parameters. Compared to the MLR approach, the non-linear RF models statistics improved in two of three data series. Analysis of the molecular descriptors selected into MLR models revealed major solvent–solute interactions, with primary contributions from Coulomb and dipolar or hydrogen bonding interactions and followed by the descriptors that expose dispersion force related interactions. Relations to all the aforementioned solvent–solute interactions were also found in RF models descriptor interpretation. Comparison of models demonstrated that a common anion in different ILs produces a significant correlation between the data series log K values, while that of ILs with a common cation are less but still significantly correlated. The lower correlation could be attributed to varying structural differences in the corresponding ions, or the anion might have a more substantial role in determining partition properties with the organic solutes in the series.

Published
2021
Full Text
View/download PDF

45. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Author

Geven Piir, Paola Gramatica, Vinicius M. Alves, Uko Maran, Xianliang Qiao, Michel Petitjean, Christopher M. Grulke, Karl-Werner Schramm, Giuseppe Felice Mangiatordi, Antony J. Williams, Barun Bhhatarai, Sherif Farag, Alexey V. Zakharov, Chetan Rupakheti, Emilio Benfenati, Weida Tong, Chandrabose Selvaraj, Eva Bay Wedebye, Fang Bai, Sugunadevi Sakkiah, Nicole Kleinstreuer, Ann M. Richard, Orazio Nicolotti, Huixiao Hong, George Van Den Driessche, Ilya A. Balabin, Eugene N. Muratov, Imran Shah, Ulf Norinder, Jiazhong Li, Huanxiang Liu, Viviana Consonni, Igor V. Tetko, Pavel V. Pogodin, Ruili Huang, Ester Papa, Ahmed Abdelaziz, Dragos Horvath, Alexandre Varnek, Patricia Ruiz, Carolina Horta Andrade, Domenico Alberga, Ziye Zheng, Roberto Todeschini, Daniela Trisciuzzi, Nina Jeliazkova, Xuehua Li, Dac-Trung Nguyen, Gilles Marcou, Zhongyu Wang, Kamel Mansouri, Alexander Tropsha, Yun Tang, Alessandro Sangion, Todd M. Martin, Xin Hu, Scott Boyer, Hongbin Xie, Serena Manganelli, Richard S. Judson, Nikolai Georgiev Nikolov, Jingwen Chen, Denis Fourches, Vladimir Poroikov, Alfonso T. García-Sosa, Alessandra Roncaglioni, Davide Ballabio, Patrik L. Andersson, Sulev Sild, Francesca Grisoni, Lixia Sun, Olivier Taboureau, US Environmental Protection Agency (EPA), University of Insubria, Varese, Laboratoire de Chémoinformatique, Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Università degli studi di Bari, Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, National Institute of Environmental Health Sciences, National Institutes of Health, University of Bari Aldo Moro (UNIBA), Federal University of Goiás [Jataí], Dept. of Statistics - University of North Carolina - Chapel Hill, University of North Carolina System (UNC)-University of North Carolina System (UNC), Umeå University, Lanzhou University, Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Istituto di Ricerche Farmacologiche 'Mario Negri', Karolinska Institutet [Stockholm], Dalian University of Technology, Department of Statistics University of Milano Bicocca, University of North Carolina at Chapel Hill (UNC), North Carolina State University [Raleigh] (NC State), Institute of Computer Science [University of Tartu, Estonie], University of Tartu, U.S. ENVIRONMENTAL PROTECTION AGENCY USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), U.S. Food and Drug Administration (FDA), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), National Institutes of Health [Bethesda] (NIH), Università degli studi di Bari Aldo Moro (UNIBA), Technical University of Denmark [Lyngby] (DTU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institute of Biomedical Chemistry [Moscou] (IBMC), Centers for Disease Control and Prevention, The University of Chicago Medicine [Chicago], East China University of Science and Technology, and German Research Center for Environmental Health - Helmholtz Center München (GmbH)

Subjects
Databases, Factual, Health, Toxicology and Mutagenesis, Computational biology, Pharmacology and Toxicology, 010501 environmental sciences, Biology, Endocrine Disruptors, 01 natural sciences, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, CHIM/01 - CHIMICA ANALITICA, High-Throughput Screening Assays, consensu, Endocrine system, Humans, Computer Simulation, 030212 general & internal medicine, United States Environmental Protection Agency, 0105 earth and related environmental sciences, QSAR, Research, Public Health, Environmental and Occupational Health, Farmakologi och toxikologi, United States, 3. Good health, Metabolic pathway, machine learning, chemistry, 13. Climate action, Receptors, Androgen, chemical modelling, Androgen Receptor, Androgens, Xenobiotic, Androgen receptor activity, [CHIM.CHEM]Chemical Sciences/Cheminformatics, Hormone
Abstract

Background:Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.Objectives:In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).Methods:The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.Results:The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.Discussion:The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program’s Integrated Chemical Environment. https://doi.org/10.1289/EHP5580

Published
2020
Full Text
View/download PDF

46. Storing and Using Qualitative and Quantitative Structure–Activity Relationships in the Era of Toxicological and Chemical Data Expansion

Author

Sulev Sild, Uko Maran, Daniel Neagu, and Geven Piir

Subjects
Structure (mathematical logic), Knowledge extraction, Mathematical model, Computer science, business.industry, Big data, Quantitative structure, Chemical data, business, USable, Representation (mathematics), Data science
Abstract

Emerging Big Data technologies and the growing amount of data in predictive toxicology (and in chemistry in general) require new solutions and methods for large-scale data and model storage, as well as for model representation and analysis. Knowledge extraction from big and diverse toxicology and chemistry data results in mathematical models that are used to organise and systematise data and structure patterns. Consequently, next to the developments in data organisation and analysis, the systematic representation and organisation of descriptive and predictive qualitative and quantitative structure–activity relationships, (Q)SARs, is equally important. Therefore, full attention from model developers is required to make the new knowledge derived from the data and models easily accessible and usable. This chapter considers issues related to the organisation of (Q)SAR models and gives an overview of the file and data formats used to organise predictive models as well as their storage solutions in the era of data expansion.

Published
2019
Full Text
View/download PDF

47. Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances

Author

Alfonso T. García-Sosa and Uko Maran

Subjects
0301 basic medicine, Quantitative Structure-Activity Relationship, Endocrine Disruptors, Ligands, chimp, 0302 clinical medicine, Protein structure, androgen receptor, rat, Biology (General), Spectroscopy, chemical fingerprints, Molecular Structure, General Medicine, Ligand (biochemistry), Computer Science Applications, Molecular Docking Simulation, Chemistry, Receptors, Androgen, 030220 oncology & carcinogenesis, docking, Protein Binding, QH301-705.5, Bayesian probability, Computational biology, Molecular Dynamics Simulation, Biology, Article, Catalysis, Inorganic Chemistry, Androgen receptor binding, 03 medical and health sciences, Naive Bayes classifier, Humans, human, Physical and Theoretical Chemistry, QD1-999, Molecular Biology, multivariate logistic regression, Organic Chemistry, Reproducibility of Results, toxicity, Bayes Theorem, ecfp, Androgen receptor, Logistic Models, 030104 developmental biology, ROC Curve, Docking (molecular), bayesian, Androgen receptor activity
Abstract

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.

Published
2021
Full Text
View/download PDF

49. A role of flavonoids in cytochrome c-cardiolipin interactions

Author

Ekaterina A. Korobkova, Kelley Samuels, Alicia K. Williams, Malaysha Rice, Uko Maran, Mare Oja, Bokey Wong, Jenny Fong, and Anne-Marie Sapse

Subjects
Membrane permeability, Cardiolipins, Clinical Biochemistry, Pharmaceutical Science, 01 natural sciences, Biochemistry, Flavones, Structure-Activity Relationship, chemistry.chemical_compound, Flavonols, Drug Discovery, Cardiolipin, Humans, Enzyme Inhibitors, Inner mitochondrial membrane, Molecular Biology, Flavonoids, chemistry.chemical_classification, Dose-Response Relationship, Drug, Molecular Structure, biology, 010405 organic chemistry, Cytochrome c, Organic Chemistry, Cytochromes c, Catechin, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, chemistry, biology.protein, Molecular Medicine, Oxidation-Reduction, Peroxidase
Abstract

The processes preceding the detachment of cytochrome c (cyt c) from the inner mitochondrial membrane in intrinsic apoptosis involve peroxidation of cardiolipin (CL) catalyzed by cyt c-CL complex. In the present work, we studied the effect of 17 dietary flavonoids on the peroxidase activity of cyt c bound to liposomes. Specifically, we explored the relationship between peroxidase activity and flavonoids' (1) potential to modulate cyt c unfolding, (2) effect on the oxidation state of heme iron, (3) membrane permeability, (4) membrane binding energy, and (5) structure. The measurements revealed that flavones, flavonols, and flavanols were the strongest, while isoflavones were the weakest inhibitors of the oxidation. Flavonoids' peroxidase inhibition activity correlated positively with their potential to suppress Trp-59 fluorescence in cyt c as well as the number of OH groups. Hydrophilic flavonoids, such as catechin, having the lowest membrane permeability and the strongest binding with phosphocholine (PC) based on the quantum chemical calculations exhibited the strongest inhibition of Amplex Red (AR) peroxidation, suggesting a membrane-protective function of flavonoids at the surface. The results of the present research specify basic principles for the design of molecules that will control the catalytic oxidation of lipids in mitochondrial membranes. These principles take into account the number of hydroxyl groups and hydrophilicity of flavonoids.

Published
2021
Full Text
View/download PDF

50. Effects of temperature and concentration on particle size in a lactose solution using dynamic light scattering analysis

Author

Tanel Kaart, V. Poikalainen, Uko Maran, Avo Karus, A. Pisponen, and H. Mootse

Subjects
Supersaturation, 0402 animal and dairy science, Nucleation, Thermodynamics, 04 agricultural and veterinary sciences, 02 engineering and technology, 021001 nanoscience & nanotechnology, 040201 dairy & animal science, Applied Microbiology and Biotechnology, law.invention, chemistry.chemical_compound, Crystallography, chemistry, Dynamic light scattering, law, Cluster (physics), Molecule, Particle size, Crystallization, Lactose, 0210 nano-technology, Food Science
Abstract

Nucleation, as well as the processes affecting it, is a constant source of interest for scientists dealing with crystallisation. The majority of published material focuses on secondary nucleation because of the complexity of the crystallisation process. The influence of temperature and different levels of concentration, from highly supersaturated solutions down to lactose solutions below the supersaturated level, on lactose primary cluster was investigated using the dynamic light scattering (DLS) method. The minimum particle size in the solution was found; it varied from 0.89 to 1.17 nm and matched the size of a single lactose molecule (1.18 nm), which was estimated by using molecular modelling techniques. Using the DLS technique, it was confirmed that particle size increased with the decrease of temperature and increasing of concentration of the solution. Theoretical calculations were made to estimate a predicted number of molecules that form particles of primary clusters with different sizes.

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results