AI-ACCELERATED DRUG DISCOVERY

Probable ATP-dependent RNA helicase DDX6

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Probable ATP-dependent RNA helicase DDX6 - 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 Probable ATP-dependent RNA helicase DDX6 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 Probable ATP-dependent RNA helicase DDX6 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 Probable ATP-dependent RNA helicase DDX6, 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 Probable ATP-dependent RNA helicase DDX6. 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 Probable ATP-dependent RNA helicase DDX6. 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 Probable ATP-dependent RNA helicase DDX6 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.

Probable ATP-dependent RNA helicase DDX6

partner:

Reaxense

upacc:

P26196

UPID:

DDX6_HUMAN

Alternative names:

ATP-dependent RNA helicase p54; DEAD box protein 6; Oncogene RCK

Alternative UPACC:

P26196; Q5D048

Background:

Probable ATP-dependent RNA helicase DDX6, also known as ATP-dependent RNA helicase p54, DEAD box protein 6, and Oncogene RCK, plays a pivotal role in RNA metabolism. It is essential for the formation of P-bodies, cytoplasmic structures involved in the storage and regulation of mRNAs. DDX6 coordinates the storage of translationally inactive mRNAs, preventing their degradation and plays a role in mRNA decapping. Additionally, it blocks autophagy in nutrient-rich conditions by repressing the expression of ATG-related genes.

Therapeutic significance:

DDX6 is implicated in Intellectual developmental disorder with impaired language and dysmorphic facies, a condition characterized by intellectual disability, developmental delay, and dysmorphic features. Understanding the role of DDX6 could open doors to potential therapeutic strategies for this disorder.

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