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 Peptidyl-prolyl cis-trans isomerase FKBP14 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 Peptidyl-prolyl cis-trans isomerase FKBP14 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 Peptidyl-prolyl cis-trans isomerase FKBP14, 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 Peptidyl-prolyl cis-trans isomerase FKBP14. 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 Peptidyl-prolyl cis-trans isomerase FKBP14. 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 Peptidyl-prolyl cis-trans isomerase FKBP14 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.
Peptidyl-prolyl cis-trans isomerase FKBP14
partner:
Reaxense
upacc:
Q9NWM8
UPID:
FKB14_HUMAN
Alternative names:
22 kDa FK506-binding protein; FK506-binding protein 14; Rotamase
Alternative UPACC:
Q9NWM8
Background:
Peptidyl-prolyl cis-trans isomerase FKBP14, also known as the 22 kDa FK506-binding protein, plays a crucial role in protein folding with a preference for substrates containing 4-hydroxylproline modifications. This protein targets various collagen types, including type III, VI, and X, essential for connective tissue integrity.
Therapeutic significance:
FKBP14's involvement in Ehlers-Danlos syndrome, kyphoscoliotic type 2, underscores its therapeutic significance. This condition, characterized by connective tissue disorders, highlights the protein's potential as a target for developing treatments aimed at ameliorating symptoms and improving patient quality of life.
