NF-kappa-B essential modulator

NF-kappa-B essential modulator - Focused Library Design

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 NF-kappa-B essential modulator 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 NF-kappa-B essential modulator 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 NF-kappa-B essential modulator, 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 NF-kappa-B essential modulator. 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 NF-kappa-B essential modulator. 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 NF-kappa-B essential modulator 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.

NF-kappa-B essential modulator

partner:

Reaxense

upacc:

Q9Y6K9

UPID:

NEMO_HUMAN

Alternative names:

FIP-3; IkB kinase-associated protein 1; Inhibitor of nuclear factor kappa-B kinase subunit gamma; NF-kappa-B essential modifier

Alternative UPACC:

Q9Y6K9; Q7LBY6; Q7Z7F1

Background:

NF-kappa-B essential modulator (NEMO), also known as Inhibitor of nuclear factor kappa-B kinase subunit gamma, plays a pivotal role in the NF-kappa-B signaling pathway. This pathway is crucial for immune response, cell survival, and inflammation. NEMO facilitates the activation of NF-kappa-B by mediating the phosphorylation and degradation of its inhibitors. It uniquely binds to both 'Lys-63'-linked and linear polyubiquitin, with a preference for the latter, highlighting its versatile role in cellular signaling.

Therapeutic significance:

NEMO is implicated in a range of diseases, including Ectodermal dysplasia and immunodeficiency, Immunodeficiency 33, Incontinentia pigmenti, and systemic autoinflammatory disease. These conditions underscore the protein's critical role in immune regulation and development. Understanding NEMO's function could pave the way for innovative treatments targeting these genetic disorders, offering hope for affected individuals.