AI-ACCELERATED DRUG DISCOVERY

Ubiquitin-conjugating enzyme E2 E2

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Ubiquitin-conjugating enzyme E2 E2 - 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 Ubiquitin-conjugating enzyme E2 E2 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 Ubiquitin-conjugating enzyme E2 E2 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 Ubiquitin-conjugating enzyme E2 E2, 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 Ubiquitin-conjugating enzyme E2 E2. 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 Ubiquitin-conjugating enzyme E2 E2. 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 Ubiquitin-conjugating enzyme E2 E2 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.

Ubiquitin-conjugating enzyme E2 E2

partner:

Reaxense

upacc:

Q96LR5

UPID:

UB2E2_HUMAN

Alternative names:

E2 ubiquitin-conjugating enzyme E2; UbcH8; Ubiquitin carrier protein E2; Ubiquitin-protein ligase E2

Alternative UPACC:

Q96LR5

Background:

The Ubiquitin-conjugating enzyme E2 E2, known alternatively as E2 ubiquitin-conjugating enzyme E2, UbcH8, Ubiquitin carrier protein E2, and Ubiquitin-protein ligase E2, plays a pivotal role in protein ubiquitination. It accepts ubiquitin from the E1 complex and catalyzes its covalent attachment to other proteins, facilitating 'Lys-11'-, 'Lys-48'-, and 'Lys-63'-linked polyubiquitination. This enzyme also catalyzes the ISGylation of influenza A virus NS1 protein, showcasing its versatility in post-translational modifications.

Therapeutic significance:

Understanding the role of Ubiquitin-conjugating enzyme E2 E2 could open doors to potential therapeutic strategies, particularly in targeting viral infections and regulating protein degradation pathways.

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