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 Vesicular glutamate transporter 3 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 Vesicular glutamate transporter 3 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 Vesicular glutamate transporter 3, 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 Vesicular glutamate transporter 3. 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 Vesicular glutamate transporter 3. 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 Vesicular glutamate transporter 3 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.
Vesicular glutamate transporter 3
partner:
Reaxense
upacc:
Q8NDX2
UPID:
VGLU3_HUMAN
Alternative names:
Solute carrier family 17 member 8
Alternative UPACC:
Q8NDX2; B3KXZ6; B7ZKV4; Q17RQ8
Background:
Vesicular glutamate transporter 3 (VGLUT3), encoded by the gene with the accession number Q8NDX2, is a multifunctional transporter. It is pivotal in transporting L-glutamate and various ions like chloride, sodium, and phosphate across membranes. Its primary role at the synaptic vesicle membrane is as an uniporter facilitating L-glutamate uptake into synaptic vesicles, crucial for neurotransmission in excitatory neural cells. Additionally, VGLUT3 functions as a chloride channel and, following exocytosis, as a symporter for Na(+) and phosphate, aiding in synaptic phosphate homeostasis.
Therapeutic significance:
The association of VGLUT3 with autosomal dominant deafness, 25 (DFNA25), underscores its clinical relevance. This condition, characterized by non-syndromic sensorineural hearing loss, highlights the therapeutic potential of targeting VGLUT3. Understanding the role of VGLUT3 could open doors to potential therapeutic strategies for hearing loss and other neurological conditions.
