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We conceived the Halogen-Enriched Fragment Library (HEFLib) to investigate the potential of halogen bonds in the early stages of drug discovery. As the number of competitive interactions increases with ligand size, we reasoned that a binding mode relying on halogen bonding is more likely for fragments than highly decorated molecules. Thus, fragments could feature unexplored binding modes. We screened the HEFLib against the human kinase DYRK1a and verified micromolar binding fragments via isothermal titration calorimetry (ITC). The crystal structure of one fragment revealed a noncanonical binding mode, despite the fragment’s classical hinge binding motif. In addition, the fragment occupies a secondary binding site. Both binding modes feature a halogen bond, which we evaluated by ab initio calculations. Structure–affinity relationship (SAR) from a set of analogues improves the affinity, provides a promising fragment-growth vector, and highlights the benefits and applicability of halogen bonds in early lead development.

Fragment screening of the challenging drug target T-p53-Y220C with our diversity optimized HEFLib leads to diverse reversible and covalent binding modes.

The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline.

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