Focused On-demand Library for Methyltransferase-like protein 17, mitochondrial

Details

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.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We utilise our cutting-edge, exclusive workflow to develop focused 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.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

PARTNER
Receptor.AI
 
UPACC
Q9H7H0

UPID:
MET17_HUMAN

ALTERNATIVE NAMES:
False p73 target gene protein; Methyltransferase 11 domain-containing protein 1; Protein RSM22 homolog, mitochondrial

ALTERNATIVE UPACC:
Q9H7H0; Q9BSH1; Q9BZH2; Q9BZH3

BACKGROUND:
The Methyltransferase-like protein 17, mitochondrial, also referred to as Protein RSM22 homolog, is indispensable for mitochondrial biogenesis and function. It serves as an RNA methyltransferase, crucial for the integrity and operation of the mitochondrial small ribosomal subunit, thereby facilitating efficient protein synthesis within mitochondria.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Methyltransferase-like protein 17 offers a promising avenue for developing novel therapeutic approaches. Given its essential role in mitochondrial protein translation, targeting this protein could yield breakthroughs in treating diseases linked to mitochondrial dysfunction.

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