Focused On-demand Library for Ubiquitin-conjugating enzyme E2 J1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

E2 ubiquitin-conjugating enzyme J1; Non-canonical ubiquitin-conjugating enzyme 1; Yeast ubiquitin-conjugating enzyme UBC6 homolog E

Alternative UPACC:

Q9Y385; A8K3F9; Q53F25; Q5W0N4; Q9BZ32; Q9NQL3; Q9NY66; Q9P011; Q9P0S0; Q9UF10


Ubiquitin-conjugating enzyme E2 J1, also known as E2 ubiquitin-conjugating enzyme J1, plays a pivotal role in protein ubiquitination, essential for selective degradation of misfolded proteins and recovery from ER stress. It is involved in translational control of TNF-alpha synthesis and positions the endosomal system perinuclearly by mediating ubiquitination of SQSTM1. Additionally, it regulates IFN-beta signaling and mediates 'Lys-48'-linked ubiquitination on IRF3, promoting Dengue virus RNA replication.

Therapeutic significance:

Understanding the role of Ubiquitin-conjugating enzyme E2 J1 could open doors to potential therapeutic strategies.

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