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 Volume-regulated anion channel subunit LRRC8A 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 Volume-regulated anion channel subunit LRRC8A 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 Volume-regulated anion channel subunit LRRC8A, 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 Volume-regulated anion channel subunit LRRC8A. 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 Volume-regulated anion channel subunit LRRC8A. 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 Volume-regulated anion channel subunit LRRC8A 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.
Volume-regulated anion channel subunit LRRC8A
partner:
Reaxense
upacc:
Q8IWT6
UPID:
LRC8A_HUMAN
Alternative names:
Leucine-rich repeat-containing protein 8A; Swelling protein 1
Alternative UPACC:
Q8IWT6; Q6UXM2; Q8NCI0; Q9P2B1
Background:
Volume-regulated anion channel subunit LRRC8A, also known as Leucine-rich repeat-containing protein 8A or Swelling protein 1, plays a pivotal role in cellular osmoregulation. It forms an essential component of the VRAC channel, facilitating the transport of ions and organic osmolytes to maintain cell volume. LRRC8A is crucial for amino acid efflux under osmotic stress and mediates the uptake of cisplatin, a chemotherapy drug. It also transports immune messengers and is involved in B-cell and T-cell development, myoblast differentiation, and glucose-sensing in pancreatic beta cells.
Therapeutic significance:
LRRC8A's involvement in Agammaglobulinemia 5, an immunodeficiency disorder, underscores its therapeutic potential. Targeting LRRC8A could lead to novel treatments for this condition and enhance our understanding of B-cell development, offering insights into broader immunological therapies.
