AI-ACCELERATED DRUG DISCOVERY

Nuclear factor of activated T-cells, cytoplasmic 2

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Nuclear factor of activated T-cells, cytoplasmic 2 - 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 Nuclear factor of activated T-cells, cytoplasmic 2 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 Nuclear factor of activated T-cells, cytoplasmic 2 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 Nuclear factor of activated T-cells, cytoplasmic 2, 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 Nuclear factor of activated T-cells, cytoplasmic 2. 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 Nuclear factor of activated T-cells, cytoplasmic 2. 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 Nuclear factor of activated T-cells, cytoplasmic 2 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.

Nuclear factor of activated T-cells, cytoplasmic 2

partner:

Reaxense

upacc:

Q13469

UPID:

NFAC2_HUMAN

Alternative names:

NFAT pre-existing subunit; T-cell transcription factor NFAT1

Alternative UPACC:

Q13469; B5B2N8; B5B2N9; B5B2P0; B5B2P2; B5B2P3; Q13468; Q5TFW7; Q5TFW8; Q9NPX6; Q9NQH3; Q9UJR2

Background:

Nuclear factor of activated T-cells, cytoplasmic 2 (NFATC2), also known as NFAT pre-existing subunit and T-cell transcription factor NFAT1, plays a pivotal role in T-cell activation and the inducible expression of cytokine genes, including IL-2, IL-3, IL-4, TNF-alpha, and GM-CSF. It also promotes invasive migration through GPC6 expression and the WNT5A signaling pathway, and negatively regulates chondrogenesis.

Therapeutic significance:

NFATC2's involvement in joint contractures, osteochondromas, and B-cell lymphoma highlights its potential as a therapeutic target. Understanding the role of NFATC2 could open doors to potential therapeutic strategies for these conditions.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.