Focused On-demand Library for HLA class I histocompatibility antigen, alpha chain E

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We utilise our cutting-edge, exclusive workflow to develop focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.







Alternative names:

MHC class I antigen E

Alternative UPACC:

P13747; E2G051; Q30169; Q6DU44; Q9BT83; Q9GIY7; Q9GIY8


HLA class I histocompatibility antigen, alpha chain E (HLA-E), a non-classical major histocompatibility class Ib molecule, plays a crucial role in immune self-nonself discrimination. It forms a complex with B2M/beta-2-microglobulin to bind self-peptides from classical MHC class Ia molecules, functioning as a ligand for NK cell inhibitory receptors, thus enabling NK cells to tolerate self. HLA-E's interaction with peptides from stress-induced chaperones or viral proteins alters its recognition by NK cells, impacting immune response.

Therapeutic significance:

Understanding the role of HLA-E could open doors to potential therapeutic strategies, especially considering its involvement in immune evasion mechanisms of pathogens like HIV-1, human cytomegalovirus, and SARS-CoV-2. Its ability to modulate NK cell activity offers a promising avenue for enhancing antiviral and antitumor immunity.

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