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 DNA polymerase alpha subunit B 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 DNA polymerase alpha subunit B 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 DNA polymerase alpha subunit B, 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 DNA polymerase alpha subunit B. 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 DNA polymerase alpha subunit B. 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 DNA polymerase alpha subunit B 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.
DNA polymerase alpha subunit B
partner:
Reaxense
upacc:
Q14181
UPID:
DPOA2_HUMAN
Alternative names:
DNA polymerase alpha 70 kDa subunit
Alternative UPACC:
Q14181; B4DNB4; Q9BPV3
Background:
The DNA polymerase alpha subunit B, also known as the DNA polymerase alpha 70 kDa subunit, is a crucial component of the DNA polymerase alpha complex. This complex is essential for the initiation of DNA synthesis during the S phase of the cell cycle. It is composed of a catalytic subunit POLA1, an accessory subunit POLA2, and two primase subunits, PRIM1 and PRIM2. The complex is recruited to DNA at replicative forks, initiating DNA synthesis by oligomerizing short RNA primers on both leading and lagging strands.
Therapeutic significance:
Understanding the role of DNA polymerase alpha subunit B could open doors to potential therapeutic strategies.
