Focused On-demand Library for Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase 3

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.


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


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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.


 

Fig. 1. The screening 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
O94766

UPID:
B3GA3_HUMAN

ALTERNATIVE NAMES:
Beta-1,3-glucuronyltransferase 3; Glucuronosyltransferase I; UDP-GlcUA:Gal beta-1,3-Gal-R glucuronyltransferase

ALTERNATIVE UPACC:
O94766; B7ZAB3; Q96I06; Q9UEP0

BACKGROUND:
The enzyme UDP-GlcUA:Gal beta-1,3-Gal-R glucuronyltransferase, known for its role in the biosynthesis of glycosaminoglycans, is essential for the formation of the linkage tetrasaccharide in proteoglycans. Its activity is crucial for the proper synthesis of heparan sulfate and chondroitin sulfate, transferring glucuronic acid to the trisaccharide Gal-beta-1,3-Gal-beta-1,4-Xyl. This specificity underlines its critical function in cellular processes.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in a disease characterized by multiple joint dislocations and craniofacial dysmorphism, the therapeutic potential of targeting UDP-GlcUA:Gal beta-1,3-Gal-R glucuronyltransferase is significant. Exploring its function further could lead to innovative treatments for related congenital disorders.

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