AI-ACCELERATED DRUG DISCOVERY

tRNA methyltransferase 10 homolog C

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

tRNA methyltransferase 10 homolog C - Focused Library Design

Available from Reaxense

This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of tRNA methyltransferase 10 homolog C including:

1. LLM-powered literature research

Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into tRNA methyltransferase 10 homolog C therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.

 Fig. 1. Preliminary target research workflow

2. AI-Driven Conformational Ensemble Generation

Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of tRNA methyltransferase 10 homolog C, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.

 Fig. 2. AI-powered molecular dynamics simulations workflow

3. Binding pockets identification and characterization

We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.

 Fig. 3. AI-based binding pocket detection workflow

4. AI-Powered Virtual Screening

Our ecosystem is equipped to perform AI-driven virtual screening on tRNA methyltransferase 10 homolog C. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of tRNA methyltransferase 10 homolog C. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.

 Fig. 4. The screening workflow of Receptor.AI

Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.

The focused library for tRNA methyltransferase 10 homolog C 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.

tRNA methyltransferase 10 homolog C

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.

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