AI-ACCELERATED DRUG DISCOVERY

ER membrane protein complex subunit 10

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

ER membrane protein complex subunit 10 - 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 ER membrane protein complex subunit 10 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 ER membrane protein complex subunit 10 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 ER membrane protein complex subunit 10, 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 ER membrane protein complex subunit 10. 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 ER membrane protein complex subunit 10. 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 ER membrane protein complex subunit 10 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.

ER membrane protein complex subunit 10

partner:

Reaxense

upacc:

Q5UCC4

UPID:

EMC10_HUMAN

Alternative names:

Hematopoietic signal peptide-containing membrane domain-containing protein 1

Alternative UPACC:

Q5UCC4; Q5UCC6; Q69YT5; Q6UWP3; Q86YL4; Q8N541

Background:

ER membrane protein complex subunit 10, also known as Hematopoietic signal peptide-containing membrane domain-containing protein 1, plays a crucial role in the endoplasmic reticulum membrane. It facilitates the insertion of newly synthesized membrane proteins, accommodating those with weakly hydrophobic or destabilizing features. This protein is essential for the cotranslational and post-translational insertion of multi-pass and tail-anchored proteins, respectively, influencing the topology of multi-pass membrane proteins like G protein-coupled receptors.

Therapeutic significance:

Linked to Neurodevelopmental disorder with dysmorphic facies and variable seizures, ER membrane protein complex subunit 10's understanding could pave the way for innovative therapeutic strategies targeting neurodevelopmental disorders.

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