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 NEDD8-conjugating enzyme UBE2F 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 NEDD8-conjugating enzyme UBE2F 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 NEDD8-conjugating enzyme UBE2F, 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 NEDD8-conjugating enzyme UBE2F. 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 NEDD8-conjugating enzyme UBE2F. 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 NEDD8-conjugating enzyme UBE2F 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.
NEDD8-conjugating enzyme UBE2F
partner:
Reaxense
upacc:
Q969M7
UPID:
UBE2F_HUMAN
Alternative names:
NEDD8 carrier protein UBE2F; NEDD8 protein ligase UBE2F; NEDD8-conjugating enzyme 2; RING-type E3 NEDD8 transferase UBE2F; Ubiquitin-conjugating enzyme E2 F
Alternative UPACC:
Q969M7; A8K1Z8; B4DDT9; B4DFI1; B4DMK3; B4DZU2; B8ZZG2; C9J212; H9KVB9
Background:
NEDD8-conjugating enzyme UBE2F plays a pivotal role in protein modification, specifically in the process of neddylation. It accepts the ubiquitin-like protein NEDD8 from the UBA3-NAE1 E1 complex and catalyzes its attachment to target proteins. This enzyme's interaction with E3 ubiquitin ligase RBX2, as opposed to RBX1, indicates a selective mechanism for neddylating specific proteins such as CUL5.
Therapeutic significance:
Understanding the role of NEDD8-conjugating enzyme UBE2F could open doors to potential therapeutic strategies.