Focused On-demand Library for tRNA methyltransferase 10 homolog C

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.

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.

Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

Our library distinguishes itself through several key aspects:

The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q7L0Y3

UPID:

TM10C_HUMAN

Alternative names:

HBV pre-S2 trans-regulated protein 2; Mitochondrial ribonuclease P protein 1; RNA (guanine-9-)-methyltransferase domain-containing protein 1; Renal carcinoma antigen NY-REN-49; mRNA methyladenosine-N(1)-methyltransferase; tRNA (adenine(9)-N(1))-methyltransferase; tRNA (guanine(9)-N(1))-methyltransferase

Alternative UPACC:

Q7L0Y3; Q9NRG5; Q9NX54; Q9Y596

Background:

tRNA methyltransferase 10 homolog C (TRMT10C) plays a pivotal role in mitochondrial tRNA maturation, crucial for protein synthesis and mitochondrial function. It is part of the mitochondrial ribonuclease P complex, involved in tRNA cleavage and methylation, essential for cellular energy production. TRMT10C's alternative names include Mitochondrial ribonuclease P protein 1 and mRNA methyladenosine-N(1)-methyltransferase, reflecting its diverse functions in RNA processing.

Therapeutic significance:

TRMT10C is linked to Combined oxidative phosphorylation deficiency 30, a severe mitochondrial disorder. Understanding TRMT10C's role could lead to novel therapeutic strategies for treating mitochondrial diseases, highlighting the importance of research in this area.