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 Tumor necrosis factor alpha-induced protein 3 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 Tumor necrosis factor alpha-induced protein 3 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 Tumor necrosis factor alpha-induced protein 3, 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 Tumor necrosis factor alpha-induced protein 3. 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 Tumor necrosis factor alpha-induced protein 3. 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 Tumor necrosis factor alpha-induced protein 3 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.
Tumor necrosis factor alpha-induced protein 3
partner:
Reaxense
upacc:
P21580
UPID:
TNAP3_HUMAN
Alternative names:
OTU domain-containing protein 7C; Putative DNA-binding protein A20; Zinc finger protein A20
Alternative UPACC:
P21580; B2R767; E1P588; Q2HIX9; Q5VXQ7; Q9NSR6
Background:
Tumor necrosis factor alpha-induced protein 3, also known as A20, plays a pivotal role in regulating inflammatory responses and immune system functions. It exhibits both ubiquitin ligase and deubiquitinase activities, crucial for terminating NF-kappa-B activity and ensuring the transient nature of inflammatory signaling pathways. A20's ability to modulate ubiquitination processes across various signaling pathways underscores its importance in cellular homeostasis and defense mechanisms.
Therapeutic significance:
A20's involvement in Autoinflammatory syndrome, familial, Behcet-like 1, highlights its potential as a therapeutic target. Understanding A20's regulatory mechanisms offers promising avenues for developing treatments for autoinflammatory disorders, emphasizing the need for innovative drug discovery efforts to harness its therapeutic potential.