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 Voltage-gated purine nucleotide uniporter SLC17A9 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 Voltage-gated purine nucleotide uniporter SLC17A9 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 Voltage-gated purine nucleotide uniporter SLC17A9, 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 Voltage-gated purine nucleotide uniporter SLC17A9. 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 Voltage-gated purine nucleotide uniporter SLC17A9. 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 Voltage-gated purine nucleotide uniporter SLC17A9 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.
Voltage-gated purine nucleotide uniporter SLC17A9
partner:
Reaxense
upacc:
Q9BYT1
UPID:
S17A9_HUMAN
Alternative names:
Solute carrier family 17 member 9; Vesicular nucleotide transporter
Alternative UPACC:
Q9BYT1; B3KTF2; Q5W198; Q8TB07; Q8TBP4; Q8TEL5; Q9BYT0; Q9BYT2
Background:
The Voltage-gated purine nucleotide uniporter SLC17A9, also known as Solute carrier family 17 member 9 and Vesicular nucleotide transporter, plays a pivotal role in cellular energy management. It facilitates the transport of ATP, ADP, and GTP across membranes, utilizing the membrane potential to drive ATP accumulation in lysosomes and secretory vesicles. This process is crucial for the regulation of ATP-dependent proteins within these organelles and indirectly influences the exocytosis of ATP, impacting various physiological functions.
Therapeutic significance:
Given its involvement in Porokeratosis 8, disseminated superficial actinic type, a disorder linked to faulty keratinization leading to potential cutaneous neoplasms, understanding the role of SLC17A9 could open doors to potential therapeutic strategies. Targeting SLC17A9's function might offer novel approaches for managing this skin disorder and its associated neoplastic risks.
