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 CD151 antigen 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 CD151 antigen 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 CD151 antigen, 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 CD151 antigen. 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 CD151 antigen. 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 CD151 antigen 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.
CD151 antigen
partner:
Reaxense
upacc:
P48509
UPID:
CD151_HUMAN
Alternative names:
GP27; Membrane glycoprotein SFA-1; Platelet-endothelial tetraspan antigen 3; Tetraspanin-24
Alternative UPACC:
P48509; A8KAK8; E9PI15; Q14826; Q86U54; Q96TE3
Background:
CD151 antigen, also known as Tetraspanin-24, plays a crucial role in the assembly of kidney basement membranes. It is identified by alternative names such as GP27, Membrane glycoprotein SFA-1, and Platelet-endothelial tetraspan antigen 3. This protein is also involved in the endocytosis of human papillomavirus 16, indicating its significance in microbial infection processes.
Therapeutic significance:
CD151 antigen's involvement in Epidermolysis bullosa simplex 7, characterized by skin blistering, nephropathy, and deafness, highlights its therapeutic potential. Understanding the role of CD151 antigen could open doors to potential therapeutic strategies for treating this complex disorder.
