Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis.

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target-compound pairings and were able to suggest targets for all 309 active compounds in the database.

Structural basis for c-di-AMP-dependent regulation of the bacterial stringent response by receptor protein DarB.

The bacterial second messenger c-di-AMP controls essential cellular processes, including potassium and osmolyte homeostasis. This makes synthesizing enzymes and components involved in c-di-AMP signal transduction intriguing as potential targets for drug development. The c-di-AMP receptor protein DarB of Bacillus subtilis binds the Rel protein and triggers the Rel-dependent stringent response to stress conditions; however, the structural basis for this trigger is unclear. Here, we report crystal structures of DarB in the ligand-free state and of DarB complexed with c-di-AMP, 3'3'-cGAMP, and AMP. We show that DarB forms a homodimer with a parallel, head-to-head assembly of the monomers. We also confirm the DarB dimer binds two cyclic dinucleotide molecules or two AMP molecules; only one adenine of bound c-di-AMP is specifically recognized by DarB, while the second protrudes out of the donut-shaped protein. This enables DarB to bind also 3'3'-cGAMP, as only the adenine fits in the active site. In absence of c-di-AMP, DarB binds to Rel and stimulates (p)ppGpp synthesis, whereas the presence of c-di-AMP abolishes this interaction. Furthermore, the DarB crystal structures reveal no conformational changes upon c-di-AMP binding, leading us to conclude the regulatory function of DarB on Rel must be controlled directly by the bound c-di-AMP. We thus derived a structural model of the DarB-Rel complex via in silico docking, which was validated with mass spectrometric analysis of the chemically crosslinked DarB-Rel complex and mutagenesis studies. We suggest, based on the predicted complex structure, a mechanism of stringent response regulation by c-di-AMP.

Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR.

Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR, which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics.

Mapping Compound Databases to Disease Maps-A MINERVA Plugin for CandActBase.

The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA's own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online.

CFTR Lifecycle Map-A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations.

Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics.