AI-ACCELERATED DRUG DISCOVERY

Chloride channel protein ClC-Ka

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Chloride channel protein ClC-Ka - 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 Chloride channel protein ClC-Ka 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 Chloride channel protein ClC-Ka 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 Chloride channel protein ClC-Ka, 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 Chloride channel protein ClC-Ka. 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 Chloride channel protein ClC-Ka. 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 Chloride channel protein ClC-Ka 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.

Chloride channel protein ClC-Ka

partner:

Reaxense

upacc:

P51800

UPID:

CLCKA_HUMAN

Alternative names:

ClC-K1

Alternative UPACC:

P51800; B4DPD3; E7EPH6; Q5T5P8; Q5T5Q4; Q7Z6D1; Q86VT1

Background:

The Chloride channel protein ClC-Ka, also known as ClC-K1, plays a pivotal role in the regulation of cell volume, membrane potential stabilization, signal transduction, and transepithelial transport. Its significance is underscored in urinary concentrating mechanisms, highlighting its essential function in maintaining electrolyte balance.

Therapeutic significance:

ClC-Ka's involvement in Bartter syndrome 4B, a condition characterized by impaired salt reabsorption, hypokalemic metabolic alkalosis, and sensorineural deafness, underscores its therapeutic potential. Targeting ClC-Ka could lead to innovative treatments for this syndrome, emphasizing the importance of understanding its biological mechanisms.

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