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 Tudor-interacting repair regulator protein 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 Tudor-interacting repair regulator protein 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 Tudor-interacting repair regulator protein, 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 Tudor-interacting repair regulator protein. 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 Tudor-interacting repair regulator protein. 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 Tudor-interacting repair regulator protein 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.
Tudor-interacting repair regulator protein
partner:
Reaxense
upacc:
Q9BRJ7
UPID:
TIRR_HUMAN
Alternative names:
NUDT16-like protein 1; Protein syndesmos
Alternative UPACC:
Q9BRJ7; Q8NAI2
Background:
Tudor-interacting repair regulator protein (TIRR), also known as NUDT16-like protein 1 or Protein syndesmos, plays a pivotal role in DNA damage response. It regulates TP53BP1 by stabilizing it and controlling its chromatin recruitment. TIRR prevents TP53BP1 chromatin binding in the absence of DNA damage by interacting with TP53BP1's Tudor-like domain, thus keeping TP53BP1 in the nucleus. Upon DNA damage, TIRR's dissociation from TP53BP1, facilitated by ATM-induced TP53BP1 phosphorylation and RIF1 recruitment, allows TP53BP1 to bind to DNA double-strand breaks. Additionally, TIRR binds U8 snoRNA.
Therapeutic significance:
Understanding the role of Tudor-interacting repair regulator protein could open doors to potential therapeutic strategies.
