AI-ACCELERATED DRUG DISCOVERY

Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13 - 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13, 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13. 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13. 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 Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13 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.

Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13

partner:

Reaxense

upacc:

Q9NP73

UPID:

ALG13_HUMAN

Alternative names:

Asparagine-linked glycosylation 13 homolog; Glycosyltransferase 28 domain-containing protein 1; UDP-N-acetylglucosamine transferase subunit ALG13 homolog

Alternative UPACC:

Q9NP73; B1AKD6; B1AKM1; B2R5L5; B7Z6J0; B7Z804; B7Z847; B7Z9A8; B7ZAJ1; B7ZB57; Q17RC3; Q5JXY9; Q9H5U8

Background:

The Putative bifunctional UDP-N-acetylglucosamine transferase and deubiquitinase ALG13 is a protein of interest due to its dual functionality. It is known to potentially possess both glycosyltransferase and deubiquitinase activities. This protein is involved in the critical process of protein N-glycosylation, specifically in the second step of the dolichol-linked oligosaccharide pathway. Its alternative names include Asparagine-linked glycosylation 13 homolog, Glycosyltransferase 28 domain-containing protein 1, and UDP-N-acetylglucosamine transferase subunit ALG13 homolog.

Therapeutic significance:

ALG13 is linked to Developmental and Epileptic Encephalopathy 36 (DEE36), a severe condition characterized by refractory seizures and neurodevelopmental impairment. The disease underscores the protein's crucial role in embryonic development and cell function maintenance. Understanding the role of ALG13 could open doors to potential therapeutic strategies for DEE36 and related congenital disorders of glycosylation.

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