AI-ACCELERATED DRUG DISCOVERY
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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A 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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A 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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A, 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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A. 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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A. 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 Interferon-inducible double-stranded RNA-dependent protein kinase activator A 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.
Interferon-inducible double-stranded RNA-dependent protein kinase activator A
partner:
Reaxense
upacc:
O75569
UPID:
PRKRA_HUMAN
Alternative names:
PKR-associated protein X; PKR-associating protein X; Protein activator of the interferon-induced protein kinase; Protein kinase, interferon-inducible double-stranded RNA-dependent activator
Alternative UPACC:
O75569; A8K3I6; Q53G24; Q6X7T5; Q8NDK4
Background:
Interferon-inducible double-stranded RNA-dependent protein kinase activator A, also known as PKR-associated protein X, plays a pivotal role in cellular defense mechanisms. It activates EIF2AK2/PKR in the absence of double-stranded RNA, leading to phosphorylation of EIF2S1/EFI2-alpha, inhibiting translation and inducing apoptosis. This protein is essential for siRNA production by DICER1 and subsequent siRNA-mediated gene silencing, although it is not required for pre-miRNA to miRNA processing by DICER1. It also promotes UBC9-p53/TP53 association, sumoylation, and phosphorylation of p53/TP53, enhancing its activity in a EIF2AK2/PKR-dependent manner.
Therapeutic significance:
The association of Interferon-inducible double-stranded RNA-dependent protein kinase activator A with Dystonia 16, a dystonia-parkinsonism disorder, underscores its therapeutic significance. Understanding the role of this protein could lead to novel therapeutic strategies for managing Dystonia 16, characterized by sustained involuntary muscle contraction and parkinsonian features.
