AI-ACCELERATED DRUG DISCOVERY

Lysosomal alpha-glucosidase

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Lysosomal alpha-glucosidase - Focused Library Design

Available from Reaxense

This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of Lysosomal alpha-glucosidase including:

1. LLM-powered literature research

Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into Lysosomal alpha-glucosidase therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.

 Fig. 1. Preliminary target research workflow

2. AI-Driven Conformational Ensemble Generation

Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of Lysosomal alpha-glucosidase, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.

 Fig. 2. AI-powered molecular dynamics simulations workflow

3. Binding pockets identification and characterization

We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.

 Fig. 3. AI-based binding pocket detection workflow

4. AI-Powered Virtual Screening

Our ecosystem is equipped to perform AI-driven virtual screening on Lysosomal alpha-glucosidase. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of Lysosomal alpha-glucosidase. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.

 Fig. 4. The screening workflow of Receptor.AI

Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.

The focused library for Lysosomal alpha-glucosidase 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.

Lysosomal alpha-glucosidase

partner:

Reaxense

upacc:

P10253

UPID:

LYAG_HUMAN

Alternative names:

Acid maltase; Aglucosidase alfa

Alternative UPACC:

P10253; Q09GN4; Q14351; Q16302; Q8IWE7

Background:

Lysosomal alpha-glucosidase, also known as Acid maltase or Aglucosidase alfa, plays a pivotal role in the breakdown of glycogen within lysosomes. It primarily targets alpha-1,4-linked glycosidic linkages but also has the capability to hydrolyze alpha-1,6-linked glucans. This enzyme's activity is crucial for the proper degradation of glycogen, a key energy storage molecule in cells.

Therapeutic significance:

Glycogen storage disease 2, also known as Pompe disease, is directly linked to mutations affecting the gene encoding Lysosomal alpha-glucosidase. This disorder ranges from severe infantile forms with cardiomyopathy and muscular hypotonia to adult forms characterized by limb-girdle muscular dystrophy. Understanding the enzymatic function and genetic regulation of Lysosomal alpha-glucosidase could lead to targeted therapies for this metabolic disorder.

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