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 DNA-dependent metalloprotease SPRTN 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 DNA-dependent metalloprotease SPRTN 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 DNA-dependent metalloprotease SPRTN, 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 DNA-dependent metalloprotease SPRTN. 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 DNA-dependent metalloprotease SPRTN. 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 DNA-dependent metalloprotease SPRTN 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.
DNA-dependent metalloprotease SPRTN
partner:
Reaxense
upacc:
Q9H040
UPID:
SPRTN_HUMAN
Alternative names:
DNA damage protein targeting VCP; Protein with SprT-like domain at the N terminus
Alternative UPACC:
Q9H040; B1AKT0; B5MEF7; Q5TE78; Q6UWW6; Q96BC5; Q96KA0
Background:
The DNA-dependent metalloprotease SPRTN plays a crucial role in maintaining genomic integrity by mediating the proteolytic cleavage of covalent DNA-protein cross-links (DPCs) during DNA synthesis. These DPCs, induced by agents like UV light or formaldehyde, are highly toxic and interfere with vital chromatin transactions. SPRTN's association with the DNA replication machinery and its specific removal of DPCs underscore its importance in the cellular response to DNA damage.
Therapeutic significance:
Given its pivotal role in genomic stability and its involvement in Ruijs-Aalfs syndrome, characterized by genomic instability and early onset hepatocellular carcinoma, targeting SPRTN could offer novel therapeutic avenues. Understanding the role of DNA-dependent metalloprotease SPRTN could open doors to potential therapeutic strategies.