Focused On-demand Library for tRNA N(3)-methylcytidine methyltransferase METTL6

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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:

Methyltransferase-like protein 6

Alternative UPACC:

Q8TCB7; Q96LU4


The tRNA N(3)-methylcytidine methyltransferase METTL6, also known as Methyltransferase-like protein 6, plays a crucial role in the post-transcriptional modification of tRNA. It specifically mediates the N(3)-methylcytidine modification of residue 32 in the anticodon loop of tRNA(Ser), a process essential for the accurate translation of the genetic code into proteins. This modification is facilitated through its interaction with SARS1/SerRS, highlighting a complex regulatory mechanism.

Therapeutic significance:

Understanding the role of tRNA N(3)-methylcytidine methyltransferase METTL6 could open doors to potential therapeutic strategies. Its precise function in tRNA modification suggests a foundational role in protein synthesis, offering a novel angle for targeting diseases at the genetic translation level.

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