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 RNA-binding protein with multiple splicing 2 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 RNA-binding protein with multiple splicing 2 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 RNA-binding protein with multiple splicing 2, 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 RNA-binding protein with multiple splicing 2. 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 RNA-binding protein with multiple splicing 2. 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 RNA-binding protein with multiple splicing 2 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.
RNA-binding protein with multiple splicing 2
partner:
Reaxense
upacc:
Q6ZRY4
UPID:
RBPS2_HUMAN
Alternative names:
RNA binding protein, mRNA processing factor 2
Alternative UPACC:
Q6ZRY4; A2RRG0
Background:
RNA-binding protein with multiple splicing 2 (RBPMS2) plays a pivotal role in the gastrointestinal system, specifically in smooth muscle cell differentiation and proliferation. It binds to NOG mRNA, a key inhibitor of the bone morphogenetic protein (BMP) pathway, facilitating an increase in NOG mRNA levels. This action negatively regulates the BMP signaling pathway, promoting reversible dedifferentiation and proliferation of smooth muscle cells.
Therapeutic significance:
Understanding the role of RNA-binding protein with multiple splicing 2 could open doors to potential therapeutic strategies.
