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 E3 ubiquitin-protein ligase SMURF2 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 E3 ubiquitin-protein ligase SMURF2 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 E3 ubiquitin-protein ligase SMURF2, 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 E3 ubiquitin-protein ligase SMURF2. 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 E3 ubiquitin-protein ligase SMURF2. 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 E3 ubiquitin-protein ligase SMURF2 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.
E3 ubiquitin-protein ligase SMURF2
partner:
Reaxense
upacc:
Q9HAU4
UPID:
SMUF2_HUMAN
Alternative names:
HECT-type E3 ubiquitin transferase SMURF2; SMAD ubiquitination regulatory factor 2; SMAD-specific E3 ubiquitin-protein ligase 2
Alternative UPACC:
Q9HAU4; Q52LL1; Q9H260
Background:
E3 ubiquitin-protein ligase SMURF2 plays a pivotal role in cellular processes by transferring ubiquitin to substrates, affecting their degradation and signaling pathways. It regulates TGF-beta signaling through interaction with SMAD7, leading to SMAD7-mediated receptor degradation. Additionally, SMURF2 targets SMAD1 and SMAD2 for degradation, modulating their activity in cellular processes. Its interaction with viral proteins, such as Ebola's VP40, highlights its role in microbial infection by facilitating virus budding.
Therapeutic significance:
Understanding the role of E3 ubiquitin-protein ligase SMURF2 could open doors to potential therapeutic strategies. Its involvement in TGF-beta signaling and interaction with viral proteins presents it as a target for therapeutic intervention in diseases related to these pathways.
