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 tRNA-splicing endonuclease subunit Sen2 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 tRNA-splicing endonuclease subunit Sen2 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 tRNA-splicing endonuclease subunit Sen2, 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 tRNA-splicing endonuclease subunit Sen2. 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 tRNA-splicing endonuclease subunit Sen2. 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 tRNA-splicing endonuclease subunit Sen2 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.
tRNA-splicing endonuclease subunit Sen2
partner:
Reaxense
upacc:
Q8NCE0
UPID:
SEN2_HUMAN
Alternative names:
tRNA-intron endonuclease Sen2
Alternative UPACC:
Q8NCE0; B7Z6K1; C9IZI7; G5E9Q3; Q8WTW7; Q9BPU7
Background:
The tRNA-splicing endonuclease subunit Sen2 plays a pivotal role in cellular function by ensuring the proper splicing of precursor tRNA, a critical step in the maturation of tRNA molecules necessary for protein synthesis. This enzyme, also known as tRNA-intron endonuclease Sen2, is integral to the tRNA-splicing endonuclease complex, facilitating the precise cleavage of pre-tRNA to release introns and form mature tRNA molecules. Its activity is essential for maintaining the fidelity of protein translation and overall cellular health.
Therapeutic significance:
Given its crucial role in tRNA splicing, mutations in the gene encoding tRNA-splicing endonuclease subunit Sen2 are linked to Pontocerebellar hypoplasia 2B, a severe neurological disorder. Understanding the role of tRNA-splicing endonuclease subunit Sen2 could open doors to potential therapeutic strategies for this debilitating condition, highlighting the importance of targeted research in uncovering novel treatment avenues.
