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

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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 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.

We use our state-of-the-art dedicated workflow for designing focused libraries.

Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse 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.

partner

Reaxense

upacc

Q7L5D6

UPID:

GET4_HUMAN

Alternative names:

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

Alternative UPACC:

Q7L5D6; A4D2Q1; B3KNC7; Q9UFC9; Q9Y309

Background:

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.