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 Rho-related GTP-binding protein RhoJ 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 Rho-related GTP-binding protein RhoJ 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 Rho-related GTP-binding protein RhoJ, 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 Rho-related GTP-binding protein RhoJ. 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 Rho-related GTP-binding protein RhoJ. 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 Rho-related GTP-binding protein RhoJ 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.
Rho-related GTP-binding protein RhoJ
partner:
Reaxense
upacc:
Q9H4E5
UPID:
RHOJ_HUMAN
Alternative names:
Ras-like protein family member 7B; Tc10-like GTP-binding protein
Alternative UPACC:
Q9H4E5; Q96KC1
Background:
Rho-related GTP-binding protein RhoJ, also known as Ras-like protein family member 7B and Tc10-like GTP-binding protein, plays a pivotal role in angiogenesis. This plasma membrane-associated small GTPase is essential for endothelial cell migration during vascular development, facilitated by its interaction with GLUL. RhoJ's function in eliciting the formation of F-actin-rich structures further underscores its critical role in regulating endothelial cell migration.
Therapeutic significance:
Understanding the role of Rho-related GTP-binding protein RhoJ could open doors to potential therapeutic strategies. Its involvement in angiogenesis and endothelial cell migration positions it as a key target for developing treatments aimed at vascular diseases and disorders related to impaired angiogenesis.
