Article
Refine
Document Type
- Article (4) (remove)
Language
- English (4)
Has Fulltext
- yes (4) (remove)
Is part of the Bibliography
- no (4)
Keywords
- machine learning (2)
- Biochemistry (1)
- Structural biology (1)
- big data (1)
- cell cycle (1)
- chemogenomics (1)
- data curation (1)
- de novo design (1)
- high content imaging (1)
- medicinal chemistry (1)
Institute
- Buchmann Institut für Molekulare Lebenswissenschaften (BMLS) (4) (remove)
The FUBP1-FUSE complex is an essential component of a transcription molecular machinery that is necessary for tight regulation of expression of many key genes including c-Myc and p21. FUBP1 utilizes its four articulated KH modules, which function cooperatively, for FUSE nucleotide binding. To understand molecular mechanisms fundamental to the intermolecular interaction, we present a set of crystal structures, as well ssDNA-binding characterization of FUBP1 KH domains. All KH1-4 motifs were highly topologically conserved, and were able to interact with FUSE individually and independently. Nevertheless, differences in nucleotide binding properties among the four KH domains were evident, including higher nucleotide-binding potency for KH3 as well as diverse nucleotide sequence preferences. Variations in amino acid compositions at one side of the binding cleft responsible for nucleobase resulted in diverse shapes and electrostatic charge interaction, which might feasibly be a contributing factor for different nucleotide-binding propensities among KH1-4. Nonetheless, conservation of structure and nucleotide-binding property in all four KH motifs is essential for the cooperativity of multi KH modules present in FUBP1 towards nanomolar affinity for FUSE interaction. Comprehensive structural comparison and ssDNA binding characteristics of all four KH domains presented here provide molecular insights at a fundamental level that might be beneficial for elucidating the mechanisms of the FUBP1-FUSE interaction.
Phenotypical screening is a widely used approach in drug discovery for the identification of small molecules with cellular activities. However, functional annotation of identified hits often poses a challenge. The development of small molecules with narrow or exclusive target selectivity such as chemical probes and chemogenomic (CG) libraries, greatly diminishes this challenge, but non-specific effects caused by compound toxicity or interference with basic cellular functions still pose a problem to associate phenotypic readouts with molecular targets. Hence, each compound should ideally be comprehensively characterized regarding its effects on general cell functions. Here, we report an optimized live-cell multiplexed assay that classifies cells based on nuclear morphology, presenting an excellent indicator for cellular responses such as early apoptosis and necrosis. This basic readout in combination with the detection of other general cell damaging activities of small molecules such as changes in cytoskeletal morphology, cell cycle and mitochondrial health provides a comprehensive time-dependent characterization of the effect of small molecules on cellular health in a single experiment. The developed high-content assay offers multi-dimensional comprehensive characterization that can be used to delineate generic effects regarding cell functions and cell viability, allowing an assessment of compound suitability for subsequent detailed phenotypic and mechanistic studies.
Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.
Selectivity remains a challenge for ATP-mimetic kinase inhibitors, an issue that may be overcome by targeting unique residues or binding pockets. However, to date only few strategies have been developed. Here we identify that bulky residues located N-terminal to the DFG motif (DFG-1) represent an opportunity for designing highly selective inhibitors with unexpected binding modes. We demonstrate that several diverse inhibitors exerted selective, noncanonical binding modes that exclusively target large hydrophobic DFG-1 residues present in many kinases including PIM, CK1, DAPK, and CLK. By use of the CLK family as a model, structural and biochemical data revealed that the DFG-1 valine controlled a noncanonical binding mode in CLK1, providing a rationale for selectivity over the closely related CLK3 which harbors a smaller DFG-1 alanine. Our data suggest that targeting the restricted back pocket in the small fraction of kinases that harbor bulky DFG-1 residues offers a versatile selectivity filter for inhibitor design.