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 TGF-beta receptor type-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 TGF-beta receptor type-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 TGF-beta receptor type-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 TGF-beta receptor type-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 TGF-beta receptor type-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 TGF-beta receptor type-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.
TGF-beta receptor type-1
partner:
Reaxense
upacc:
P36897
UPID:
TGFR1_HUMAN
Alternative names:
Activin A receptor type II-like protein kinase of 53kD; Activin receptor-like kinase 5; Serine/threonine-protein kinase receptor R4; TGF-beta type I receptor; Transforming growth factor-beta receptor type I
Alternative UPACC:
P36897; Q6IR47; Q706C0; Q706C1
Background:
TGF-beta receptor type-1, also known as Activin receptor-like kinase 5, plays a pivotal role in cellular processes by mediating TGF-beta cytokines TGFB1, TGFB2, and TGFB3 signals. This transmembrane serine/threonine kinase, in concert with TGFBR2, regulates cell cycle, wound healing, immunosuppression, and more through both canonical SMAD-dependent and non-canonical signaling pathways.
Therapeutic significance:
Linked to Loeys-Dietz syndrome 1 and Multiple self-healing squamous epithelioma, TGF-beta receptor type-1's involvement in these diseases underscores its potential as a therapeutic target. Understanding its role could lead to novel treatments for these and related conditions.
