Focused On-demand Library for Golgi to ER traffic protein 4 homolog

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







Alternative names:

Conserved edge-expressed protein; Transmembrane domain recognition complex 35 kDa subunit

Alternative UPACC:

Q7L5D6; A4D2Q1; B3KNC7; Q9UFC9; Q9Y309


Golgi to ER traffic protein 4 homolog, also known as Conserved edge-expressed protein and Transmembrane domain recognition complex 35 kDa subunit, plays a pivotal role in protein quality control. It maintains misfolded proteins in a soluble state, ensuring their proper delivery to the endoplasmic reticulum or their degradation by the proteasome. This protein is crucial for the post-translational delivery of tail-anchored proteins to the endoplasmic reticulum membrane, interacting with newly synthesized proteins and mediating their delivery.

Therapeutic significance:

Linked to Congenital disorder of glycosylation 2Y, Golgi to ER traffic protein 4 homolog's understanding could pave the way for innovative therapeutic strategies targeting this and potentially other glycosylation disorders.

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