AI-ACCELERATED DRUG DISCOVERY

Sodium-dependent phosphate transport protein 2A

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Sodium-dependent phosphate transport protein 2A - Focused Library Design

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 Sodium-dependent phosphate transport protein 2A 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 Sodium-dependent phosphate transport protein 2A 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 Sodium-dependent phosphate transport protein 2A, 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 Sodium-dependent phosphate transport protein 2A. 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 Sodium-dependent phosphate transport protein 2A. 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 Sodium-dependent phosphate transport protein 2A 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.

Sodium-dependent phosphate transport protein 2A

partner:

Reaxense

upacc:

Q06495

UPID:

NPT2A_HUMAN

Alternative names:

Na(+)-dependent phosphate cotransporter 2A; NaPi-3; Sodium/phosphate cotransporter 2A; Solute carrier family 34 member 1

Alternative UPACC:

Q06495; B4DPE3

Background:

Sodium-dependent phosphate transport protein 2A, also known as Na(+)-dependent phosphate cotransporter 2A, NaPi-3, and Solute carrier family 34 member 1, plays a crucial role in phosphate homeostasis. It actively transports phosphate into cells via Na(+) cotransport in the renal brush border membrane, with a Na(+):Pi stoichiometry of 3:1, making the process electrogenic.

Therapeutic significance:

This protein's malfunction is linked to diseases such as Nephrolithiasis/osteoporosis, hypophosphatemic, 1, Fanconi renotubular syndrome 2, and Hypercalcemia, infantile, 2. These conditions underscore the protein's critical role in renal phosphate absorption and the potential for targeted therapies to address phosphate homeostasis disorders.

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