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 Cytokine-like nuclear factor N-PAC 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 Cytokine-like nuclear factor N-PAC 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 Cytokine-like nuclear factor N-PAC, 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 Cytokine-like nuclear factor N-PAC. 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 Cytokine-like nuclear factor N-PAC. 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 Cytokine-like nuclear factor N-PAC 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.
Cytokine-like nuclear factor N-PAC
partner:
Reaxense
upacc:
Q49A26
UPID:
GLYR1_HUMAN
Alternative names:
3-hydroxyisobutyrate dehydrogenase-like protein; Glyoxylate reductase 1 homolog; Nuclear protein NP60; Nuclear protein of 60 kDa; Nucleosome-destabilizing factor; Putative oxidoreductase GLYR1
Alternative UPACC:
Q49A26; B4DL47; C9JJ40; C9JJ60; Q5U632; Q6P1Q2; Q6V3W7; Q9BTI1; Q9BXK2
Background:
Cytokine-like nuclear factor N-PAC, known for its diverse roles in chromatin modification and gene expression regulation, is a pivotal player in cellular biology. This protein, also referred to as 3-hydroxyisobutyrate dehydrogenase-like protein, Glyoxylate reductase 1 homolog, and several other names, exhibits a unique ability to bind histone H3 and DNA, facilitating transcriptional activation. Its interaction with KDM1B enhances histone demethylase activity, crucial for chromatin remodeling and efficient transcription through nucleosomes. N-PAC's role in heart development and stress response via MAPK14/p38alpha regulation underscores its multifunctional nature.
Therapeutic significance:
Understanding the role of Cytokine-like nuclear factor N-PAC could open doors to potential therapeutic strategies.
