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 Hemoglobin subunit 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 Hemoglobin subunit 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 Hemoglobin subunit 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 Hemoglobin subunit 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 Hemoglobin subunit 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 Hemoglobin subunit 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.
Hemoglobin subunit beta
partner:
Reaxense
upacc:
P68871
UPID:
HBB_HUMAN
Alternative names:
Beta-globin; Hemoglobin beta chain
Alternative UPACC:
P68871; A4GX73; B2ZUE0; P02023; Q13852; Q14481; Q14510; Q45KT0; Q549N7; Q6FI08; Q6R7N2; Q8IZI1; Q9BX96; Q9UCD6; Q9UCP8; Q9UCP9
Background:
Hemoglobin subunit beta, also known as Beta-globin, plays a crucial role in oxygen transport from the lungs to peripheral tissues. It is part of the hemoglobin molecule, which carries oxygen in the blood. Additionally, Beta-globin is involved in regulating blood pressure through LVV-hemorphin-7, which potentiates the activity of bradykinin. It also functions as an endogenous inhibitor of enkephalin-degrading enzymes and as a selective antagonist of the P2RX3 receptor, implicating it in pain and inflammation regulation.
Therapeutic significance:
Mutations in the Hemoglobin subunit beta gene are linked to several blood disorders, including Heinz body anemias, Beta-thalassemia, Sickle cell disease, and Beta-thalassemia, dominant, inclusion body type. These conditions highlight the protein's critical role in maintaining healthy red blood cell function and structure. Understanding the molecular mechanisms of these diseases offers potential pathways for developing targeted therapies, emphasizing the importance of Hemoglobin subunit beta in medical research.
