Focused On-demand Library for HBS1-like protein

Details

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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.


Our top-notch dedicated system is used to design specialised libraries for enzymes.


 

Fig. 1. The screening 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.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

PARTNER
Receptor.AI
 
UPACC
Q9Y450

UPID:
HBS1L_HUMAN

ALTERNATIVE NAMES:
ERFS

ALTERNATIVE UPACC:
Q9Y450; B7Z365; Q4VX89; Q4VX90; Q5T7G3; Q8NDW9; Q9UPW3

BACKGROUND:
HBS1-like protein, bearing the alternative name ERFS, is integral to the Pelota-HBS1L complex, a key player in the No-Go Decay (NGD) pathway. This pathway is essential for cellular health, as it addresses ribosomal stalling on mRNAs, which could otherwise lead to the accumulation of dysfunctional proteins. By destabilizing mRNA within the ribosomal channel and facilitating its degradation, the HBS1-like protein ensures that protein synthesis errors are corrected promptly, safeguarding cellular function.

THERAPEUTIC SIGNIFICANCE:
The exploration of HBS1-like protein's function offers a promising avenue for drug discovery. Given its central role in the NGD pathway, manipulating its activity could provide a novel approach to treating conditions associated with aberrant protein synthesis and accumulation. The development of molecules that can modulate the activity of the HBS1-like protein or its pathway components could revolutionize the treatment of such diseases.

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