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 Melanocortin-2 receptor accessory protein 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 Melanocortin-2 receptor accessory protein 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 Melanocortin-2 receptor accessory protein, 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 Melanocortin-2 receptor accessory protein. 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 Melanocortin-2 receptor accessory protein. 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 Melanocortin-2 receptor accessory protein 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.
Melanocortin-2 receptor accessory protein
partner:
Reaxense
upacc:
Q8TCY5
UPID:
MRAP_HUMAN
Alternative names:
B27; Fat cell-specific low molecular weight protein; Fat tissue-specific low MW protein
Alternative UPACC:
Q8TCY5; Q5EBR3; Q8TDB7; Q8WXC1; Q8WXC2
Background:
The Melanocortin-2 Receptor Accessory Protein (MRAP), known by alternative names such as B27 and Fat tissue-specific low MW protein, plays a pivotal role in modulating melanocortin receptors (MC1R to MC5R). It enhances ligand sensitivity and cAMP generation, crucial for MC2R trafficking and ACTH response in adrenal cells, and may influence adipocyte intracellular pathways.
Therapeutic significance:
Linked to Glucocorticoid deficiency 2, a disorder marked by adrenal insufficiency and cortisol production failure, understanding MRAP's function could unveil new therapeutic avenues. Its involvement in ACTH receptor modulation and adrenal cortex activity highlights its potential in treating cortisol deficiency-related conditions.
