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Biology subjects

Boxman, F. J.

Publications and source records attributed to Boxman, F. J..

2 recordsLinked to original sources

PROXINAUT: An Automation Platform for Rapid Hit-to-Degrader Discovery

Massive library-based platforms enable rapid early hit discovery, yet subsequent hit resynthesis and validation remain major bottlenecks in drug discovery. This delay is amplified when optimizing hits, or elaborating structures towards more advanced modalities, such as chemical inducers of proximity (CIPs). Here, we report PROXINAUT, an end-to-end workflow integrating self-encoded library-based hit discovery with an automated parallel solid-phase synthesis platform to streamline hit discovery, validation, optimization, and degrader development. As a demonstration, screening a 176k-member benzimidazole library against BRD4 yielded six hit candidates, which were obtained via parallel automated synthesis and validated as submicromolar binders. The automation workflow empowered a rapid structure activity relationship exploration around the whole scaffold and resulted in a variant with six fold improved binding affinity. Next, we aimed to automatize the development of bifunctional degraders. Solid phase synthesis typically leaves a C-terminal amide as a synthetic artifact that can compromise druglike properties. We developed a strategy to repurpose this resin-attachment site, converting it into functional degrons featuring either FBXO31-targeting C-terminal amides or cereblon-binding cyclic imides. Operating without human intervention, our automated platform executes up to 16 synthetic steps across multiple parallel structures, enabling true de novo synthesis of full bifunctional scaffolds, where ligand, linker and E3 recruiter variations can all be explored within the same workflow. This resulted in cell-active BRD4 degraders with subnanomolar to nanomolar potency. Overall, we show how library selections can merge with multi-step parallel automation to convert hits directly into validated leads and advanced degrader modalities.

bioengineering↗

Generative Design of High-Affinity Peptides Using BindCraft

Discovering high-affinity ligands directly from protein structures remains a key challenge in drug discovery. We applied BindCraft, a structure-guided generative modeling platform, to de novo design of peptide ligands for protein interfaces. Originally developed for miniprotein binders, we evaluated its use for shorter peptides (10-20mers) as peptides hold greater synthetic accessibility and therapeutic potential. For the oncoprotein MDM2, BindCraft generated 70 unique peptides; 15 were synthesized, and 7 showed specific binding with nanomolar affinities (KD = 65-165 nM). Competition assays confirmed site-specific binding for the intended target site. For another oncology target, WDR5, peptides were designed for the MLL (WIN) and MYC (WBM) sites. Of the peptides tested for each site, no validated hits were found for the WIN site, but six candidates bound the WBM site with sub-micromolar affinity (KD = 219-650 nM). Based on Bindcrafts structural prediction of the binding interface, we designed a stapled variant of the best WDR5 binder, improving the potency by 6-fold to a KD of 39 nM. Overall, our findings establish BindCraft as a powerful and accessible platform for structure-based peptide discovery, with remaining limitations, but with a promising success rate even for challenging targets.

biochemistry↗