Focused On-demand Library for EEF1A lysine methyltransferase 3

Focused On-demand Libraries - Receptor.AI 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.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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

PARTNER
Receptor.AI
 
UPACC
Q96AZ1

UPID:
EFMT3_HUMAN

ALTERNATIVE NAMES:
Hepatocellular carcinoma-associated antigen 557a; Methyltransferase-like protein 21B; Protein-lysine methyltransferase METTL21B; eEF1A-KMT3

ALTERNATIVE UPACC:
Q96AZ1; Q9H749; Q9Y3W2

BACKGROUND:
The protein EEF1A lysine methyltransferase 3, known among scientists as eEF1A-KMT3, is a key enzyme in the post-translational modification of elongation factors EEF1A1 and EEF1A2. By selectively methylating 'Lys-165', EEF1AKMT3 ensures the proper functioning of these factors in protein synthesis, a process vital for cell survival and adaptation under various stress conditions.

THERAPEUTIC SIGNIFICANCE:
The exploration of EEF1A lysine methyltransferase 3's function offers a promising avenue for drug discovery. Given its regulatory role in mRNA translation and response to cellular stress, targeting EEF1AKMT3 could lead to innovative treatments for conditions characterized by abnormal protein synthesis.

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