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 ATP-dependent Clp protease proteolytic subunit, mitochondrial 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 ATP-dependent Clp protease proteolytic subunit, mitochondrial 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 ATP-dependent Clp protease proteolytic subunit, mitochondrial, 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 ATP-dependent Clp protease proteolytic subunit, mitochondrial. 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 ATP-dependent Clp protease proteolytic subunit, mitochondrial. 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 ATP-dependent Clp protease proteolytic subunit, mitochondrial 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.
ATP-dependent Clp protease proteolytic subunit, mitochondrial
partner:
Reaxense
upacc:
Q16740
UPID:
CLPP_HUMAN
Alternative names:
Endopeptidase Clp
Alternative UPACC:
Q16740; B2R4W5
Background:
The ATP-dependent Clp protease proteolytic subunit, mitochondrial, also known as Endopeptidase Clp, plays a crucial role in cellular homeostasis. It is part of the Clp complex, which is responsible for the ATP-dependent cleavage of peptides and various proteins. This protease has a broad spectrum of activity, including the degradation of specific substrates such as CSN1S1, CSN2, and CSN3, and plays a central role in the mitochondrial quality control by cleaving PINK1.
Therapeutic significance:
Perrault syndrome 3, a disorder linked to mutations in the gene encoding this protein, highlights its clinical importance. Understanding the role of ATP-dependent Clp protease proteolytic subunit could open doors to potential therapeutic strategies for treating Perrault syndrome 3 and related mitochondrial dysfunctions.
