Curated database of candidate therapeutics for the activation of CFTR-mediated ion conductance

CandActBase logo - Subtitle CandActCFTR with systems biology map

Aims of this collaborative project

Cystic fibrosis (CF) is a genetic disease, caused by CFTR which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body.

CandActCFTR is a curated compound database which annotates the chemical structure library with information on where and how in the protein life cycle a compound likely interacts, thus comprising a good starting point for modelling the disease and enhancing ligand based approaches (e.g. via eu-openscreen).

Our application CandActBase, adaptable also to other use cases than CFTR compound analysis, and the CandActCFTR data content is provided at

In the current extension of CandActCFTR, this ligand-based approach will be complemented by structure-based annotations, including the means to predict the interactions between CandActCFTR substances and CFTR by using existing molecular dynamics trajectories, and by adding more organisation and annotation modules. This approach will help in ranking putative therapeutic substances according to their potential to bind CFTR. Moreover, it will be further extended by integrating results from high-throughput screens from collaborators, helping to rank putative therapeutic substances.

Furthermore CandActCFTR will use public gene expression data to assess transcriptome profiles of CandActCFTR substances and compare differentially expressed genes to gene sets with known relevance for CFTR function, helping to rank putative therapeutic substances according to their potential to modify the cellular transcriptome in favor of CFTR function via Göttingen institutes curated TRANScription FACtor database - TRANSFAC.

Funded by

Deutsche Forschungsgemeinschaft (DFG) - Projekt number 315063128

Currently attached thesis projects

Cystic Fibrosis as a model use case for implementing cell based disease models in systems medicine

- PhD Thesis Liza Vinhoven

Original Paper

Vinhoven, L.; Stanke, F.; Hafkemeyer, S.; Nietert, M.M. CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations.Int. J. Mol. Sci. 2021, 22(14), 7590;; accepted Jul 2021


    example of a CFTR lifecylce map in coarse resolution showing an overview of compartments
    example of a CFTR lifecylce map in coarse resolution showing an overview of compartments

    Using text mining to annotate and construct biological disease maps - Topics modelling

    - Master Thesis Malte Voskamp

    • Automated Paper Information retrival and model annotation - Testing if using topics modelling for text sources can help curate a data base like CandActCFTR
    • Testing how similar the flags can be derived from the literature compared to manual gold standard
    • Automatically extracting topics from the paper stacks and using this to annotate the data
    • Providing links to the source positions - Text mining - to extend the annotation network

    Project partners

    Department of Medical Bioinformatics (UMG)
    Dr. Manuel Nietert

    Medizinische Hochschule Hannover (MHH)
    Zentrum für Kinderheilkunde und Jugendmedizin

    Klinik für Pädiatrische Pneumologie, Allergologie und Neonatologie
    Privatdozentin Dr. Frauke Stanke

    Mukoviszidose Institut gGmbH
    Dr. Sylvia Hafkemeyer

    Press releases

    Press release from the Mukoviszidose Institut – gemeinnützige Gesellschaft für Forschung und Therapieentwicklung mbH: (2019) second funding (2016) first funding

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