AI-ACCELERATED DRUG DISCOVERY

Transcription factor p65

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Transcription factor p65 - 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 Transcription factor p65 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 Transcription factor p65 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 Transcription factor p65, 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 Transcription factor p65. 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 Transcription factor p65. 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 Transcription factor p65 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.

Transcription factor p65

partner:

Reaxense

upacc:

Q04206

UPID:

TF65_HUMAN

Alternative names:

Nuclear factor NF-kappa-B p65 subunit; Nuclear factor of kappa light polypeptide gene enhancer in B-cells 3

Alternative UPACC:

Q04206; Q6GTV1; Q6SLK1

Background:

The Transcription factor p65, also known as Nuclear factor NF-kappa-B p65 subunit, plays a pivotal role in regulating inflammation, immunity, and cell survival. As a key component of the NF-kappa-B complex, it is involved in diverse biological processes through its ability to act as a transcriptional activator or repressor. The NF-kappa-B complex, primarily the RELA-NFKB1 heterodimer, binds to DNA at kappa-B sites, influencing gene expression related to cell growth, apoptosis, and tumorigenesis.

Therapeutic significance:

Given its central role in inflammation and immune response, the Transcription factor p65 is implicated in Autoinflammatory disease, familial, Behcet-like 3, a condition characterized by chronic mucosal lesions. Targeting the pathways involving this protein could offer novel therapeutic approaches for managing this autoinflammatory disorder and potentially other related conditions.

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