Focused On-demand Library for Transmembrane protein 43

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

The library 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.

Our high-tech, dedicated method is applied to construct targeted 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

Protein LUMA

Alternative UPACC:

Q9BTV4; Q7L4N5; Q8NC30; Q96A63; Q96F19; Q96JX0; Q9H076


Transmembrane protein 43, also known as Protein LUMA, plays a pivotal role in maintaining nuclear envelope structure and organizing protein complexes at the inner nuclear membrane. It is essential for retaining emerin at the inner nuclear membrane and modulates innate immune signaling through the cGAS-STING pathway. Additionally, it acts as a critical signaling component in NF-kappa-B activation, positioned downstream of EGFR and upstream of CARD10. It also contributes to passive conductance current in cochlear glia-like supporting cells, necessary for hearing and speech discrimination.

Therapeutic significance:

Transmembrane protein 43 is implicated in several diseases, including Arrhythmogenic right ventricular dysplasia, familial, 5, Emery-Dreifuss muscular dystrophy 7, autosomal dominant, and Auditory neuropathy, autosomal dominant 3. These associations highlight its potential as a target for therapeutic strategies aimed at treating congenital heart disease, degenerative myopathies, and sensorineural hearing loss.

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