AI-ACCELERATED DRUG DISCOVERY

5-aminolevulinate synthase, erythroid-specific, mitochondrial

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

5-aminolevulinate synthase, erythroid-specific, mitochondrial - 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial, 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial. 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial. 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 5-aminolevulinate synthase, erythroid-specific, mitochondrial 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.

5-aminolevulinate synthase, erythroid-specific, mitochondrial

partner:

Reaxense

upacc:

P22557

UPID:

HEM0_HUMAN

Alternative names:

5-aminolevulinic acid synthase 2; Delta-ALA synthase 2; Delta-aminolevulinate synthase 2

Alternative UPACC:

P22557; A8K3F0; A8K6C4; Q13735; Q5JZF5; Q8N6H3

Background:

5-aminolevulinate synthase, erythroid-specific, mitochondrial (ALAS2), also known as 5-aminolevulinic acid synthase 2, plays a pivotal role in heme biosynthesis. It catalyzes the first step in the pathway, the condensation of succinyl-CoA and glycine, forming aminolevulinic acid (ALA), a crucial precursor for heme. This process is essential for erythropoiesis, the production of red blood cells.

Therapeutic significance:

ALAS2 mutations are linked to sideroblastic anemia and X-linked dominant erythropoietic protoporphyria, diseases characterized by anemia, systemic iron overload, and photosensitivity. Understanding ALAS2's role could lead to novel treatments for these conditions, highlighting its therapeutic significance.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.