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 Transforming growth factor-beta-induced protein ig-h3 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 Transforming growth factor-beta-induced protein ig-h3 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 Transforming growth factor-beta-induced protein ig-h3, 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 Transforming growth factor-beta-induced protein ig-h3. 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 Transforming growth factor-beta-induced protein ig-h3. 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 Transforming growth factor-beta-induced protein ig-h3 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.
Transforming growth factor-beta-induced protein ig-h3
partner:
Reaxense
upacc:
Q15582
UPID:
BGH3_HUMAN
Alternative names:
Kerato-epithelin; RGD-containing collagen-associated protein
Alternative UPACC:
Q15582; D3DQB1; O14471; O14472; O14476; O43216; O43217; O43218; O43219; Q53XM1
Background:
Transforming growth factor-beta-induced protein ig-h3, also known as Kerato-epithelin and RGD-containing collagen-associated protein, plays a crucial role in cell adhesion and potentially in cell-collagen interactions. This protein's involvement in the structural integrity and function of the cornea is underscored by its association with various forms of corneal dystrophies.
Therapeutic significance:
Given its pivotal role in corneal dystrophies such as epithelial basement membrane dystrophy, Groenouw type 1, lattice type 1 and 3A, Thiel-Behnke type, Reis-Bucklers type, and Avellino type, understanding the function of Transforming growth factor-beta-induced protein ig-h3 could pave the way for innovative therapeutic strategies targeting these debilitating eye diseases.