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 Glutaminyl-peptide cyclotransferase 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 Glutaminyl-peptide cyclotransferase 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 Glutaminyl-peptide cyclotransferase, 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 Glutaminyl-peptide cyclotransferase. 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 Glutaminyl-peptide cyclotransferase. 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 Glutaminyl-peptide cyclotransferase 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.
Glutaminyl-peptide cyclotransferase
partner:
Reaxense
upacc:
Q16769
UPID:
QPCT_HUMAN
Alternative names:
Glutaminyl cyclase; Glutaminyl-tRNA cyclotransferase; Glutamyl cyclase
Alternative UPACC:
Q16769; Q16770; Q3KRG6; Q53TR4
Background:
Glutaminyl-peptide cyclotransferase, also known as Glutaminyl cyclase, plays a pivotal role in the biosynthesis of pyroglutamyl peptides. It exhibits specificity by favoring substrates that do not have acidic or tryptophan residues adjacent to the N-terminal glutaminyl residue. This enzyme is crucial for the formation of N-terminal pyroglutamate, a modification observed in various peptides, including those involved in amyloid plaque formation.
Therapeutic significance:
Understanding the role of Glutaminyl-peptide cyclotransferase could open doors to potential therapeutic strategies. Its involvement in the modification of peptides related to amyloid plaques highlights its significance in neurodegenerative diseases. Targeting this enzyme could offer a novel approach to modulate disease-associated peptide formation.
