RNA as a druggable target
RNA macromolecules are of increasing interest as potential drug targets. This new approach offers opportunities for urgently needed novel antibiotics, medicines against rare trinucleotide repeat disorders like Huntington’s disease and spinocerebellar ataxia or also in tumor therapy. However, conventional computational tools for structure-based drug design like molecular docking are based and optimized to predict protein-ligand interactions. The transfer from protein-ligand to RNA-ligand predictions needs proper validation as RNA and proteins show huge differences in their overall shape, charge, aromaticity and flexibility. In this project we aim to validate protein-based virtual screening tools for RNA-ligand docking and highlight pitfalls and considerations to make when searching for ligands of potential RNA drug targets. We further use a riboswitch aptamer-domain as a model system, not only to demonstrate the general suitability of molecular docking for RNA targets, but further elucidate binding thermodynamics and kinetics of RNA-ligand interactions in vitro with isothermal titration calorimetry (ITC), microscale thermophoresis (MST) and the novel switchSENSE® technology. Applying our computational and biophysical methods, general guidelines on how to design RNA-binding small molecules will be established.
Kallert, E.; Fischer, T. R.; Schneider, S.; Grimm, M.; Helm, M.; Kersten, C. Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the PreQ1-Riboswitch. J. Chem. Inf. Model. 2022, 62 (17), 4134–4148. https://doi.org/10.1021/acs.jcim.2c00751.
We can only predict binding affinities and recommend new ligands and inhibitors when we understand molecular recognition - and to be honest, we are not yet as good as we want to be and it is still a long road ahead towards prediction paradise. Therefore, we apply our biophysical tools like MST, ITC, SPR and switchSENSE® on different model systems to better understand molecular recognition, linking structural features to binding thermodynamics and kinetics. Exemplarily, in a recent study we discoverd that a macrocyclic ligand, is binding entropically less favorable than its acyclic counterparts - contrary to the common assumption that rigidification by cyclization is entropically favorable. And of course there are such nasty things like target dynamics and water molecules... What else is out there to discover, we do not yet fully understand?
Hammerschmidt, S. J.; Huber, S.; Braun, N. J.; Lander, M.; Steinmetzer, T.; Kersten, C. Thermodynamic Characterization of a Macrocyclic Zika Virus NS2B/NS3 Protease Inhibitor and Its Acyclic Analogs. Arch. Pharm. (Weinheim). 2022, No. November. https://doi.org/10.1002/ardp.202200518.
Kersten, C.; Fleischer, E.; Kehrein, J.; Borek, C.; Jaenicke, E.; Sotriffer, C.; Brenk, R. How to Design Selective Ligands for Highly Conserved Binding Sites: A Case Study Using N-Myristoyltransferases as a Model System. J. Med. Chem. 2020, 63 (5), 2095–2113. https://doi.org/10.1021/acs.jmedchem.9b00586.
Structure-based design and MedChem
Making sense of observed structure-activity relationships, recommending new chemotypes and performing virtual screenings is our daily bread and butter. Within and outside the Schirmeister group, we apply CADD methods to identify new ligands for targets of pharmaceutical interest. These cover proteases, RNA-modifying enzymes and many more. Within the MultiSensE project, Sabrina designs and synthesizes protease substates with high affinity and selectivity for biosensor applications. Thales works on the development of theranostics against cathepsins in the EU-HORIZON MSCA OncoProTools. And of course we do not miss the chances to implement state-of-the-art technologies and methods like ultra-large library virtual screenings and machine-learning homology models from AlphaFold.
Kersten, C.; Clower, S.; Barthels, F. Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold. J. Chem. Inf. Model. 2022. https://doi.org/10.1021/acs.jcim.2c01400.
Zimmermann, R. A.; Kersten, C.; Fischer, T. R.; Schwickert, M.; Nidoieva, Z.; Schirmeister, T. Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6. Int. J. Mol. Sci. 2023. https://doi.org/10.3390/ijms24076109.
Tools and Teaching - T'n'T
Seeing protein dynamics in a static crystal structure? With BANΔIT, a webserver was established to normalize and compare crystallographic B-factors and their changes upon ligand binding.
To easily evaluate advanced ITC experiments, ITCcalc was established. And thanks to ModeLL-M funding, many more tools are on its way! If you want to challenge your intuition for pKa-prediction, try our pKa-guesser!
Barthels, F.; Schirmeister, T.; Kersten, C. BANΔIT: B’-Factor Analysis for Drug Design and Structural Biology. Mol. Inform. 2020, 2000144, 1–7. https://doi.org/10.1002/minf.202000144.