Focused On-demand Library for Protein ERGIC-53

Available from Reaxense
Predicted by Alphafold

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.

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.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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:

ER-Golgi intermediate compartment 53 kDa protein; Gp58; Intracellular mannose-specific lectin MR60; Lectin mannose-binding 1

Alternative UPACC:

P49257; Q12895; Q8N5I7; Q9UQG1; Q9UQG2; Q9UQG3; Q9UQG4; Q9UQG5; Q9UQG6; Q9UQG7; Q9UQG8; Q9UQG9; Q9UQH0; Q9UQH1; Q9UQH2


Protein ERGIC-53, also known as Lectin mannose-binding 1, plays a pivotal role in the cellular transport system, specifically in the ER-to-Golgi trafficking of glycoproteins. This protein, encoded by the gene with the accession number P49257, is recognized for its mannose-specific lectin activity, which is crucial for the sorting and recycling of proteins and lipids. Alternative names include ER-Golgi intermediate compartment 53 kDa protein, Gp58, and Intracellular mannose-specific lectin MR60.

Therapeutic significance:

The involvement of Protein ERGIC-53 in Factor V and factor VIII combined deficiency 1, a blood coagulation disorder, underscores its therapeutic significance. Understanding the role of Protein ERGIC-53 could open doors to potential therapeutic strategies for managing and treating this coagulation disorder, offering hope for patients suffering from bleeding symptoms.

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