Focused On-demand Library for Proteasome subunit beta type-10

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance 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:

Low molecular mass protein 10; Macropain subunit MECl-1; Multicatalytic endopeptidase complex subunit MECl-1; Proteasome MECl-1; Proteasome subunit beta-2i

Alternative UPACC:

P40306; B2R5J4; Q5U098


Proteasome subunit beta type-10, also known as Low molecular mass protein 10 and several other names, plays a crucial role in the proteasome, a complex responsible for degrading unneeded or damaged proteins by proteolysis. This subunit is specifically involved in antigen processing, generating peptides that bind to class I molecules, essential for the immune response.

Therapeutic significance:

The protein is linked to Proteasome-associated autoinflammatory syndrome 5, a disorder marked by recurrent skin rashes, fever, and persistent hepatosplenomegaly. Understanding the role of Proteasome subunit beta type-10 could open doors to potential therapeutic strategies for this and related autoinflammatory conditions.

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