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 DCC-interacting protein 13-beta 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 DCC-interacting protein 13-beta 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 DCC-interacting protein 13-beta, 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 DCC-interacting protein 13-beta. 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 DCC-interacting protein 13-beta. 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 DCC-interacting protein 13-beta 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.
DCC-interacting protein 13-beta
partner:
Reaxense
upacc:
Q8NEU8
UPID:
DP13B_HUMAN
Alternative names:
Adapter protein containing PH domain, PTB domain and leucine zipper motif 2
Alternative UPACC:
Q8NEU8; B7Z411; B7Z4B0; F5GZG0; F8W1P5; Q8N4R7; Q9NVL2
Background:
DCC-interacting protein 13-beta, also known as Adapter protein containing PH domain, PTB domain and leucine zipper motif 2, plays a pivotal role in cell proliferation, immune response, endosomal trafficking, and cell metabolism. It influences various signaling pathways, including those leading to cell proliferation, immune modulation, and metabolic regulation. This protein's interaction with different membrane receptors, nuclear factors, and signaling proteins underscores its multifunctional nature.
Therapeutic significance:
Understanding the role of DCC-interacting protein 13-beta could open doors to potential therapeutic strategies.
