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 AP-2 complex subunit mu 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 AP-2 complex subunit mu 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 AP-2 complex subunit mu, 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 AP-2 complex subunit mu. 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 AP-2 complex subunit mu. 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 AP-2 complex subunit mu 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.
AP-2 complex subunit mu
partner:
Reaxense
upacc:
Q96CW1
UPID:
AP2M1_HUMAN
Alternative names:
AP-2 mu chain; Adaptin-mu2; Adaptor protein complex AP-2 subunit mu; Adaptor-related protein complex 2 subunit mu; Clathrin assembly protein complex 2 mu medium chain; Clathrin coat assembly protein AP50; Clathrin coat-associated protein AP50; HA2 50 kDa subunit; Plasma membrane adaptor AP-2 50 kDa protein
Alternative UPACC:
Q96CW1; A6NE12; D3DNT1; P20172; P53679
Background:
The AP-2 complex subunit mu, known by various names such as Adaptin-mu2 and Clathrin assembly protein complex 2 mu medium chain, plays a pivotal role in the adaptor protein complex 2 (AP-2). This complex is integral to protein transport via vesicles in different membrane traffic pathways, including clathrin-dependent endocytosis, where it aids in cargo selection and vesicle formation. AP-2 is crucial for receptor-mediated endocytosis and synaptic vesicle membrane recycling, recognizing specific motifs within transmembrane cargo molecules.
Therapeutic significance:
Given its involvement in Intellectual developmental disorder, autosomal dominant 60, with seizures, understanding the role of AP-2 complex subunit mu could open doors to potential therapeutic strategies. Its function in synaptic vesicle recycling and endocytosis underscores its potential as a target in neurological disorders.
