V-type proton ATPase 116 kDa subunit a 2

V-type proton ATPase 116 kDa subunit a 2 - 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 V-type proton ATPase 116 kDa subunit a 2 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 V-type proton ATPase 116 kDa subunit a 2 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 V-type proton ATPase 116 kDa subunit a 2, 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 V-type proton ATPase 116 kDa subunit a 2. 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 V-type proton ATPase 116 kDa subunit a 2. 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 V-type proton ATPase 116 kDa subunit a 2 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.

V-type proton ATPase 116 kDa subunit a 2

partner:

Reaxense

upacc:

Q9Y487

UPID:

VPP2_HUMAN

Alternative names:

Lysosomal H(+)-transporting ATPase V0 subunit a 2; TJ6; Vacuolar proton translocating ATPase 116 kDa subunit a isoform 2

Alternative UPACC:

Q9Y487; A8K026; Q6NUM0

Background:

V-type proton ATPase 116 kDa subunit a 2, also known as Lysosomal H(+)-transporting ATPase V0 subunit a 2, plays a crucial role in acidifying intracellular compartments and the extracellular environment. This protein is a key component of the V-ATPase complex, essential for pH regulation and glycosylation processes. It also influences iron homeostasis and cellular response to oxygen levels, impacting HIF1A stability.

Therapeutic significance:

Linked to Cutis laxa, autosomal recessive, 2A, and Wrinkly skin syndrome, V-type proton ATPase 116 kDa subunit a 2's dysfunction elucidates its potential in targeted therapy for these genetic disorders. Understanding its role could pave the way for innovative treatments, emphasizing the importance of research in this area.