Publications by authors named "Daniela Digles"

13 Publications

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WikiPathways: connecting communities.

Nucleic Acids Res 2021 01;49(D1):D613-D621

Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands.

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.
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http://dx.doi.org/10.1093/nar/gkaa1024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779061PMC
January 2021

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Authors:
Giulio Superti-Furga Daniel Lackner Tabea Wiedmer Alvaro Ingles-Prieto Barbara Barbosa Enrico Girardi Ulrich Goldmann Bettina Gürtl Kristaps Klavins Christoph Klimek Sabrina Lindinger Eva Liñeiro-Retes André C Müller Svenja Onstein Gregor Redinger Daniela Reil Vitaly Sedlyarov Gernot Wolf Matthew Crawford Robert Everley David Hepworth Shenping Liu Stephen Noell Mary Piotrowski Robert Stanton Hui Zhang Salvatore Corallino Andrea Faedo Maria Insidioso Giovanna Maresca Loredana Redaelli Francesca Sassone Lia Scarabottolo Michela Stucchi Paola Tarroni Sara Tremolada Helena Batoulis Andreas Becker Eckhard Bender Yung-Ning Chang Alexander Ehrmann Anke Müller-Fahrnow Vera Pütter Diana Zindel Bradford Hamilton Martin Lenter Diana Santacruz Coralie Viollet Charles Whitehurst Kai Johnsson Philipp Leippe Birgit Baumgarten Lena Chang Yvonne Ibig Martin Pfeifer Jürgen Reinhardt Julian Schönbett Paul Selzer Klaus Seuwen Charles Bettembourg Bruno Biton Jörg Czech Hélène de Foucauld Michel Didier Thomas Licher Vincent Mikol Antje Pommereau Frédéric Puech Veeranagouda Yaligara Aled Edwards Brandon J Bongers Laura H Heitman Ad P IJzerman Huub J Sijben Gerard J P van Westen Justine Grixti Douglas B Kell Farah Mughal Neil Swainston Marina Wright-Muelas Tina Bohstedt Nicola Burgess-Brown Liz Carpenter Katharina Dürr Jesper Hansen Andreea Scacioc Giulia Banci Claire Colas Daniela Digles Gerhard Ecker Barbara Füzi Viktoria Gamsjäger Melanie Grandits Riccardo Martini Florentina Troger Patrick Altermatt Cédric Doucerain Franz Dürrenberger Vania Manolova Anna-Lena Steck Hanna Sundström Maria Wilhelm Claire M Steppan

Nat Rev Drug Discov 2020 07;19(7):429-430

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http://dx.doi.org/10.1038/d41573-020-00056-6DOI Listing
July 2020

Accessing Public Compound Databases with KNIME.

Curr Med Chem 2020 ;27(38):6444-6457

Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.

Background: The KNIME platform offers several tools for the analysis of chem- and pharmacoinformatics data. Unless one has sufficient in-house data available for the analysis of interest, it is necessary to fetch third party data into KNIME. Many data sources offer valuable data, but including this data in a workflow is not always straightforward.

Objective: Here we discuss different ways of accessing public data sources. We give an overview of KNIME nodes for different sources, with references to available example workflows. For data sources with no individual KNIME node available, we present a general approach of accessing a web interface via KNIME. In addition, we discuss necessary steps before the data can be analysed, such as data curation, chemical standardisation and the merging of datasets.
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http://dx.doi.org/10.2174/0929867326666190801152317DOI Listing
January 2021

Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform.

F1000Res 2018 17;7:75. Epub 2018 Jan 17.

Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, The Netherlands.

Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions.  These new methods of the Open PHACTS API are available now.
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http://dx.doi.org/10.12688/f1000research.13197.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206606PMC
August 2019

Accessing the Open PHACTS Discovery Platform with Workflow Tools.

Methods Mol Biol 2018 ;1787:183-193

Janssen Research and Development, Beerse, Belgium.

The Open PHACTS Discovery Platform integrates several public databases, which can be of interest when annotating the results of a phenotypic screening campaign. Workflow tools provide easy-to-customize possibilities to access the platform. Here, we describe how to create such workflows for two different workflow tools (KNIME and Pipeline Pilot), including a protocol to annotate compounds (e.g., phenotypic screening hits) with compound classification, known protein targets, and classifications of the targets.
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http://dx.doi.org/10.1007/978-1-4939-7847-2_14DOI Listing
February 2019

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.

Nucleic Acids Res 2018 01;46(D1):D661-D667

Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.
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http://dx.doi.org/10.1093/nar/gkx1064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753270PMC
January 2018

Empowering pharmacoinformatics by linked life science data.

J Comput Aided Mol Des 2017 03 9;31(3):319-328. Epub 2016 Nov 9.

Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, 1090, Vienna, Austria.

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.
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http://dx.doi.org/10.1007/s10822-016-9990-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5385323PMC
March 2017

Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning.

J Cheminform 2016 4;8. Epub 2016 Feb 4.

Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, 1090 Vienna, Austria.

Background: The human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (P-gp) are co-expressed in many tissues and barriers, especially at the blood-brain barrier and at the hepatocyte canalicular membrane. Understanding their interplay in affecting the pharmacokinetics of drugs is of prime interest. In silico tools to predict inhibition and substrate profiles towards BCRP and P-gp might serve as early filters in the drug discovery and development process. However, to build such models, pharmacological data must be collected for both targets, which is a tedious task, often involving manual and poorly reproducible steps.

Results: Compounds with inhibitory activity measured against BCRP and/or P-gp were retrieved by combining Open Data and manually curated data from literature using a KNIME workflow. After determination of compound overlap, machine learning approaches were used to establish multi-label classification models for BCRP/P-gp. Different ways of addressing multi-label problems are explored and compared: label-powerset, binary relevance and classifiers chain. Label-powerset revealed important molecular features for selective or polyspecific inhibitory activity. In our dataset, only two descriptors (the numbers of hydrophobic and aromatic atoms) were sufficient to separate selective BCRP inhibitors from selective P-gp inhibitors. Also, dual inhibitors share properties with both groups of selective inhibitors. Binary relevance and classifiers chain allow improving the predictivity of the models.

Conclusions: The KNIME workflow proved a useful tool to merge data from diverse sources. It could be used for building multi-label datasets of any set of pharmacological targets for which there is data available either in the open domain or in-house. By applying various multi-label learning algorithms, important molecular features driving transporter selectivity could be retrieved. Finally, using the dataset with missing annotations, predictive models can be derived in cases where no accurate dense dataset is available (not enough data overlap or no well balanced class distribution).Graphical abstract.
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http://dx.doi.org/10.1186/s13321-016-0121-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4743411PMC
February 2016

The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research.

PLoS One 2014 18;9(12):e115460. Epub 2014 Dec 18.

Swiss Institute of Bioinformatics, CALIPHO Group, CMU - Rue Michel-Servet 1, 1211 Geneva 4, Switzerland.

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115460PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4270790PMC
December 2015

Drug discovery FAQs: workflows for answering multidomain drug discovery questions.

Drug Discov Today 2015 Apr 20;20(4):399-405. Epub 2014 Nov 20.

Connected Discovery Ltd, 27 Old Gloucester Street, London WC1N 3AX, UK.

Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community.
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http://dx.doi.org/10.1016/j.drudis.2014.11.006DOI Listing
April 2015

Computational models for predicting the interaction with ABC transporters.

Drug Discov Today Technol 2014 Jun;12:e69-77

There is strong evidence that ATP-binding cassette (ABC) transporters play a critical role in the pharmacokinetic and pharmacodynamic properties of many drugs and xenobiotics. Due to their pharmacological role, several computational approaches have been developed to understand and predict the interaction between ABC transporters and their ligands. Here, we provide an overview of the current state of the art of the ligand-based models that, derived from the transport and inhibitory activities of a set of ligands, have been published for ABC transporters.
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http://dx.doi.org/10.1016/j.ddtec.2014.03.007DOI Listing
June 2014

Transporter taxonomy - a comparison of different transport protein classification schemes.

Drug Discov Today Technol 2014 Jun;12:e37-46

University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria.

Currently, there are more than 800 well characterized human membrane transport proteins (including channels and transporters) and there are estimates that about 10% (approx. 2000) of all human genes are related to transport. Membrane transport proteins are of interest as potential drug targets, for drug delivery, and as a cause of side effects and drug–drug interactions. In light of the development of Open PHACTS, which provides an open pharmacological space, we analyzed selected membrane transport protein classification schemes (Transporter Classification Database, ChEMBL, IUPHAR/BPS Guide to Pharmacology, and Gene Ontology) for their ability to serve as a basis for pharmacology driven protein classification. A comparison of these membrane transport protein classification schemes by using a set of clinically relevant transporters as use-case reveals the strengths and weaknesses of the different taxonomy approaches.
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http://dx.doi.org/10.1016/j.ddtec.2014.03.004DOI Listing
June 2014

Self-Organizing Maps for In Silico Screening and Data Visualization.

Mol Inform 2011 Oct 2;30(10):838-46. Epub 2011 Sep 2.

Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria phone/fax: +43-1-4277-55110/+43-1-4277-9551.

Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.
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http://dx.doi.org/10.1002/minf.201100082DOI Listing
October 2011