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 Transportin-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 Transportin-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 Transportin-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 Transportin-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 Transportin-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 Transportin-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.
Transportin-2
partner:
Reaxense
upacc:
O14787
UPID:
TNPO2_HUMAN
Alternative names:
Karyopherin beta-2b
Alternative UPACC:
O14787; O14655; Q6IN77
Background:
Transportin-2, also known as Karyopherin beta-2b, is a pivotal protein in cellular mechanics, primarily involved in nuclear protein import as a nuclear transport receptor. It facilitates the docking of importin/substrate complexes to the nuclear pore complex (NPC), enabling their translocation through the pore via an energy-dependent, Ran-mediated mechanism. This process is crucial for maintaining the directional flow of proteins into the nucleus, a balance maintained by the differential distribution of GTP- and GDP-bound forms of Ran across the nuclear and cytoplasmic compartments.
Therapeutic significance:
The association of Transportin-2 with Intellectual developmental disorder with hypotonia, impaired speech, and dysmorphic facies underscores its critical role in neurological development and function. Understanding the molecular mechanisms by which Transportin-2 mutations lead to this disorder could pave the way for targeted therapeutic strategies, offering hope for individuals affected by this condition.
