AI-ACCELERATED DRUG DISCOVERY

Potassium channel subfamily K member 2

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Potassium channel subfamily K member 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 Potassium channel subfamily K member 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 Potassium channel subfamily K member 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 Potassium channel subfamily K member 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 Potassium channel subfamily K member 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 Potassium channel subfamily K member 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 Potassium channel subfamily K member 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.

Potassium channel subfamily K member 2

partner:

Reaxense

upacc:

O95069

UPID:

KCNK2_HUMAN

Alternative names:

Outward rectifying potassium channel protein TREK-1; TREK-1 K(+) channel subunit; Two pore domain potassium channel TREK-1; Two pore potassium channel TPKC1

Alternative UPACC:

O95069; A1Z1V3; A8K618; B2RCS4; B7ZL56; D3DTA5; Q5DP47; Q5DP48; Q9NRT2; Q9UNE3

Background:

Potassium channel subfamily K member 2 (KCNK2), also known as TREK-1, plays a pivotal role in potassium transport across the cell membrane. It operates in a phosphorylation-dependent manner, switching between a voltage-insensitive potassium leak channel and a voltage-dependent outward rectifying potassium channel. In astrocytes, KCNK2 predominantly forms heterodimeric channels with KCNK1, crucial for rapid glutamate release upon activation of specific G-protein coupled receptors.

Therapeutic significance:

Understanding the role of Potassium channel subfamily K member 2 could open doors to potential therapeutic strategies. Its involvement in potassium transport and astrocyte function suggests its potential impact on neurological conditions and emphasizes the importance of further research into its mechanisms and regulatory pathways.

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