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 Ubiquitin-conjugating enzyme E2 T 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 Ubiquitin-conjugating enzyme E2 T 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 Ubiquitin-conjugating enzyme E2 T, 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 Ubiquitin-conjugating enzyme E2 T. 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 Ubiquitin-conjugating enzyme E2 T. 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 Ubiquitin-conjugating enzyme E2 T 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.
Ubiquitin-conjugating enzyme E2 T
partner:
Reaxense
upacc:
Q9NPD8
UPID:
UBE2T_HUMAN
Alternative names:
Cell proliferation-inducing gene 50 protein; E2 ubiquitin-conjugating enzyme T; Ubiquitin carrier protein T; Ubiquitin-protein ligase T
Alternative UPACC:
Q9NPD8; Q2TU36
Background:
Ubiquitin-conjugating enzyme E2 T, also known as Cell proliferation-inducing gene 50 protein, plays a pivotal role in DNA repair by catalyzing the monoubiquitination of FANCD2, a crucial step in the Fanconi anemia pathway. It accepts ubiquitin from the E1 complex, facilitating its attachment to target proteins, including FANCL and FANCI, and may also contribute to the ubiquitination and degradation of BRCA1.
Therapeutic significance:
Given its central role in the DNA damage response pathway, particularly in Fanconi anemia complementation group T, targeting Ubiquitin-conjugating enzyme E2 T offers a promising avenue for developing novel treatments for this disorder, which is characterized by bone marrow failure, congenital abnormalities, and cancer predisposition.
