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 CAAX prenyl protease 1 homolog 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 CAAX prenyl protease 1 homolog 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 CAAX prenyl protease 1 homolog, 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 CAAX prenyl protease 1 homolog. 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 CAAX prenyl protease 1 homolog. 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 CAAX prenyl protease 1 homolog 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.
CAAX prenyl protease 1 homolog
partner:
Reaxense
upacc:
O75844
UPID:
FACE1_HUMAN
Alternative names:
Farnesylated proteins-converting enzyme 1; Prenyl protein-specific endoprotease 1; Zinc metalloproteinase Ste24 homolog
Alternative UPACC:
O75844; B3KQI7; D3DPU7; Q8NDZ8; Q9UBQ2
Background:
CAAX prenyl protease 1 homolog, also known as Farnesylated proteins-converting enzyme 1, plays a pivotal role in cellular processes by processing lamin A/LMNA and clearing translocons on the endoplasmic reticulum. Its proteolytic activity is essential for the removal of C-terminal residues of farnesylated proteins, impacting various cellular mechanisms.
Therapeutic significance:
Linked to diseases such as Mandibuloacral dysplasia with type B lipodystrophy and Restrictive dermopathy 1, CAAX prenyl protease 1 homolog's understanding could pave the way for novel therapeutic approaches targeting these rare but severe conditions.
