Focused On-demand Library for tRNA dimethylallyltransferase

Details

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


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


 

Fig. 1. The screening 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

PARTNER
Receptor.AI
 
UPACC
Q9H3H1

UPID:
MOD5_HUMAN

ALTERNATIVE NAMES:
Isopentenyl-diphosphate:tRNA isopentenyltransferase; hGRO1; tRNA isopentenyltransferase 1

ALTERNATIVE UPACC:
Q9H3H1; A1A4X7; Q3T7B5; Q5QPK5; Q5QPK6; Q6IAC9; Q96FJ3; Q96L45; Q9NXT7

BACKGROUND:
The enzyme tRNA dimethylallyltransferase, known alternatively as hGRO1, is pivotal in tRNA modification, specifically in the biosynthesis of N6-(dimethylallyl)adenosine. This modification is critical for the stability and function of tRNAs involved in protein synthesis, including those necessary for selenoprotein production, highlighting its fundamental role in cellular metabolism and mitochondrial function.

THERAPEUTIC SIGNIFICANCE:
Given its critical role in mitochondrial metabolism and its association with Combined oxidative phosphorylation deficiency 35, tRNA dimethylallyltransferase represents a promising target for therapeutic intervention. Exploring its function and mechanisms offers a pathway to novel treatments for mitochondrial disorders, potentially improving patient outcomes and quality of life.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.