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

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.

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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







Alternative names:

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

Alternative UPACC:

Q9H7H0; Q9BSH1; Q9BZH2; Q9BZH3


Methyltransferase-like protein 17, mitochondrial, known as Q9H7H0, plays a crucial role in mitochondrial function. It acts as a probable S-adenosyl-L-methionine-dependent RNA methyltransferase, essential for stabilizing the mitochondrial small ribosomal subunit (mt-SSU). This stabilization is vital for protein translation within mitochondria, highlighting its significance in cellular energy production and overall mitochondrial health.

Therapeutic significance:

Understanding the role of Methyltransferase-like protein 17 could open doors to potential therapeutic strategies. Its pivotal function in mitochondrial protein synthesis positions it as a key target for interventions aimed at mitochondrial disorders and diseases with mitochondrial dysfunction components.

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