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 RAS guanyl-releasing protein 1 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 RAS guanyl-releasing protein 1 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 RAS guanyl-releasing protein 1, 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 RAS guanyl-releasing protein 1. 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 RAS guanyl-releasing protein 1. 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 RAS guanyl-releasing protein 1 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.
RAS guanyl-releasing protein 1
partner:
Reaxense
upacc:
O95267
UPID:
GRP1_HUMAN
Alternative names:
Calcium and DAG-regulated guanine nucleotide exchange factor II; Ras guanyl-releasing protein
Alternative UPACC:
O95267; Q56CZ0; Q58G75; Q59HB1; Q5I3A8; Q6GV31; Q6NX39; Q7LDG6; Q9UI94; Q9UNN9
Background:
RAS guanyl-releasing protein 1, also known as Calcium and DAG-regulated guanine nucleotide exchange factor II, plays a pivotal role in cellular signaling. It activates Ras by facilitating the exchange of GDP for GTP, triggering the Erk/MAP kinase cascade. This protein is crucial for T-cell/B-cell development, homeostasis, and differentiation, linking antigen receptors to Ras. It also influences NK cell cytotoxicity and cytokine production through ERK and JNK pathways, and is involved in mast cell degranulation.
Therapeutic significance:
RAS guanyl-releasing protein 1 is implicated in systemic lupus erythematosus and immunodeficiency 64 with lymphoproliferation. Variants affecting this gene can lead to autoimmune system failures and increased susceptibility to infections. Understanding its role could pave the way for novel therapeutic strategies targeting these diseases.
