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 von Hippel-Lindau disease tumor suppressor 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 von Hippel-Lindau disease tumor suppressor 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 von Hippel-Lindau disease tumor suppressor, 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 von Hippel-Lindau disease tumor suppressor. 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 von Hippel-Lindau disease tumor suppressor. 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 von Hippel-Lindau disease tumor suppressor 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.
von Hippel-Lindau disease tumor suppressor
partner:
Reaxense
upacc:
P40337
UPID:
VHL_HUMAN
Alternative names:
Protein G7; pVHL
Alternative UPACC:
P40337; B2RE45; Q13599; Q6PDA9
Background:
The von Hippel-Lindau disease tumor suppressor, also known as Protein G7 or pVHL, plays a crucial role in the ubiquitination and proteasomal degradation pathway. It is part of the von Hippel-Lindau ubiquitination complex, targeting hydroxylated hypoxia-inducible factors (HIF) under normoxic conditions for degradation. pVHL is also involved in transcriptional repression through its interaction with HIF1A, HIF1AN, and histone deacetylases, and regulates mTORC1 activity by promoting the ubiquitination and degradation of RPTOR.
Therapeutic significance:
Given its pivotal role in regulating hypoxia-inducible factors and mTORC1 signaling, pVHL has significant therapeutic implications in diseases like Pheochromocytoma, von Hippel-Lindau disease, familial Erythrocytosis, and Renal cell carcinoma. Understanding the role of pVHL could open doors to potential therapeutic strategies, especially in targeting these diseases at the molecular level.
