AI-ACCELERATED DRUG DISCOVERY

Transcription initiation factor TFIID subunit 9B

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Transcription initiation factor TFIID subunit 9B - 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 initiation factor TFIID subunit 9B 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 initiation factor TFIID subunit 9B 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 initiation factor TFIID subunit 9B, 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 initiation factor TFIID subunit 9B. 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 initiation factor TFIID subunit 9B. 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 initiation factor TFIID subunit 9B 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 initiation factor TFIID subunit 9B

partner:

Reaxense

upacc:

Q9HBM6

UPID:

TAF9B_HUMAN

Alternative names:

Neuronal cell death-related protein 7; Transcription initiation factor TFIID subunit 9-like; Transcription-associated factor TAFII31L

Alternative UPACC:

Q9HBM6; B2RUZ9; Q9Y2S3

Background:

Transcription initiation factor TFIID subunit 9B, also known as Neuronal cell death-related protein 7 and Transcription-associated factor TAFII31L, plays a pivotal role in cell viability. It is involved in both transcriptional activation and repression of distinct yet overlapping gene sets, potentially influencing apoptosis. As a component of several key transcriptional complexes, including TFIID and TFTC, its function is crucial for RNA polymerase II-mediated transcription.

Therapeutic significance:

Understanding the role of Transcription initiation factor TFIID subunit 9B could open doors to potential therapeutic strategies.

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