Focused On-demand Library for tRNA N(3)-methylcytidine methyltransferase METTL2A

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

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 comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage 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 2A

Alternative UPACC:

Q96IZ6; A6NNC4; Q9H9G9; Q9NUI8; Q9P0B5


The tRNA N(3)-methylcytidine methyltransferase METTL2A, also known as Methyltransferase-like protein 2A, plays a crucial role in post-transcriptional modification of tRNA. It specifically mediates N(3)-methylcytidine modification of residue 32 in the anticodon loop of tRNA(Thr)(UGU) and tRNA(Arg)(CCU), a process essential for the accurate translation of the genetic code into proteins. This modification requires prior N6-threonylcarbamoylation of tRNA by the EKC/KEOPS complex, highlighting a sophisticated level of tRNA modification regulation.

Therapeutic significance:

Understanding the role of tRNA N(3)-methylcytidine methyltransferase METTL2A 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 through the modulation of genetic translation mechanisms.

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