UFMylation is an important biological process where proteins are post-translationally modified via the covalent attachment of the ubiquitin-like modifier (UBL), ubiquitin-fold modifier-1 (UFM1). UFMylation is analogous to ubiquitination, occurring via the transfer of UFM1 across E1-, E2-, and E3-like enzymes, represented by ubiquitin-like modifier-activating enzyme 5 (UBA5), ubiquitin-fold modifier conjugating enzyme 1 (UFC1), and UFM1-specific ligase 1 (UFL1), respectively. Recent work has shown that dysregulation of UFMylation is associated with several diseases, sparking interest in characterizing its enzymes as potential drug targets. To date, only inhibitors targeting UBA5 have been identified, but no such ligands exist for UFC1. In this study, we present the structure of UFC1 in complex with the sulfonic acid CAPS, which revealed a novel ligand-binding pocket in UFC1. Using biophysical assays, coupled with X-ray crystallographic studies of UFC1 variants, we show that binding of CAPS to UFC1 appears pH-dependent and is enhanced by Tyr42. Further, our TSA data show that other sulfa- and sulfonate-based compounds induce dose-dependent destabilization of UFC1, consistent with weak but direct interactions with the enzyme. Lastly, using a UFMylation assay, we show that CAPS, along with Tyr42, may have a limited influence on UFM1 transfer to UFC1 and, consequently, downstream UFMylation of protein substrates. Nevertheless, our data indicate that the CAPS-binding pocket may serve as a design scaffold for the development of UFC1 modulators. With UFC1 emerging as a drug target, our study provides a possible avenue for the design and development of novel UFC1-specific modulators with therapeutic potential.
A robust high-throughput screening assay based on AMP-Glo™ and a library of blood–brain barrier–permeable compounds are developed, identifying five inhibitors spanning three distinct scaffolds with low micromolar potency that establish UBA5 as a tractable enzymatic target and provide first-in-class chemical tools to pr...
Vigyasa Singh, Yuansong Wan, Zhisong Gao et al.· ACS Chemical Biology· 0 citations
In neurons, treatment with E64d lead to about 50-fold PLD3 accumulation and dysregulation of its proteolytic cleavage, while there was only a minor overall change on the whole proteome level, which suggests that their activation might be responsible for decreased PLD3 levels in neurons of patients with Alzheimer’s dise...
Manuel Hertwig, P. Kielkowski· bioRxiv· 0 citations
Deubiquitinase-targeting chimeras (DUBTACs) have emerged as a promising strategy for targeted protein stabilization, but their broader application remains limited by the scarcity of ligandable deubiquitinase recruiters. Here, we report a previously unexplored four-membered-ring OTUB1 recruiter chemotype. Through system...
Qiong Wu, Xiang-Yang Song, Li Chen et al.· bioRxiv· 0 citations
A crystal structure of METTL21A is solved in complex with 6m, providing structural insight into ligand recognition and supporting further structure-guided optimization and establishing a biochemically and structurally validated starting point for probing METTL21A function and a foundation for the development of cellula...
Hugo Kocek, Michal Vaško, M. Dejmek et al.· European journal of medicina...· 0 citations
Targeted protein degradation (TPD) represents a new paradigm in drug discovery, encompassing several innovative strategies like proteolysis targeting chimeras (PROTACs). PROTACs work by linking the target protein of interest (POI) to an E3 ubiquitin ligase, marking it for ubiquitination, and subsequent degradation by t...
Pramit Ganguly, Mrinalkanti Kundu, Tonmoy Sarkar et al.· Chemical Biology and Drug De...· 0 citations
VCP/p97 is an AAA+ ATPase that, together with its cofactors UFD1-NPL4 (p97-UN), unfolds ubiquitylated substrates to maintain cellular homeostasis. The human p97-UN complex associates with additional cofactors, but how these cofactors modulate p97-UN activity is not fully understood. Here, we screen cofactors and identi...
Pritha Dasgupta, Ian R. Kelsall, Gaurav Anand et al.· EMBO Journal· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.