Focused On-demand Library for Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

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

Q9UKM7

UPID:

MA1B1_HUMAN

Alternative names:

ER alpha-1,2-mannosidase; ER mannosidase 1; Man9GlcNAc2-specific-processing alpha-mannosidase; Mannosidase alpha class 1B member 1

Alternative UPACC:

Q9UKM7; Q5VSG3; Q9BRS9; Q9Y5K7

Background:

The Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase, known by alternative names such as ER alpha-1,2-mannosidase and ER mannosidase 1, plays a crucial role in glycoprotein quality control. It targets misfolded glycoproteins for degradation, trimming a single alpha-1,2-linked mannose residue from Man(9)GlcNAc(2) to produce Man(8)GlcNAc(2). At high enzyme concentrations in the ER quality control compartment (ERQC), it further reduces carbohydrates to Man(5-6)GlcNAc(2).

Therapeutic significance:

This protein's involvement in Rafiq syndrome, an autosomal recessive disorder characterized by impaired development, facial dysmorphism, and behavioral problems, underscores its therapeutic significance. Understanding the role of Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase could open doors to potential therapeutic strategies for this condition.