AI-ACCELERATED DRUG DISCOVERY
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 CCA tRNA nucleotidyltransferase 1, mitochondrial 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 CCA tRNA nucleotidyltransferase 1, mitochondrial 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 CCA tRNA nucleotidyltransferase 1, mitochondrial, 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 CCA tRNA nucleotidyltransferase 1, mitochondrial. 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 CCA tRNA nucleotidyltransferase 1, mitochondrial. 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 CCA tRNA nucleotidyltransferase 1, mitochondrial 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.
CCA tRNA nucleotidyltransferase 1, mitochondrial
partner:
Reaxense
upacc:
Q96Q11
UPID:
TRNT1_HUMAN
Alternative names:
Mitochondrial tRNA nucleotidyl transferase, CCA-adding; mt CCA-adding enzyme; mt tRNA CCA-diphosphorylase; mt tRNA CCA-pyrophosphorylase; mt tRNA adenylyltransferase
Alternative UPACC:
Q96Q11; A8K2Z6; B7WP13; C9JKA2; Q8ND57; Q9BS97; Q9Y362
Background:
CCA tRNA nucleotidyltransferase 1, mitochondrial, plays a pivotal role in the synthesis and repair of tRNA molecules by adding the essential 3'-terminal CCA sequence. This enzyme, known by alternative names such as mt CCA-adding enzyme, is crucial for the attachment of amino acids to tRNA, facilitating protein synthesis. Its activity involves using CTP and ATP as substrates to catalyze this addition, which is vital for both tRNA processing and repair.
Therapeutic significance:
The enzyme's dysfunction is linked to diseases such as Sideroblastic anemia with B-cell immunodeficiency, periodic fevers, and developmental delay, and Retinitis pigmentosa and erythrocytic microcytosis. These associations underscore the enzyme's therapeutic significance, suggesting that targeting its pathway could lead to novel treatments for these conditions.
