AI-ACCELERATED DRUG DISCOVERY
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 Glycine dehydrogenase (decarboxylating), 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 Glycine dehydrogenase (decarboxylating), 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 Glycine dehydrogenase (decarboxylating), 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 Glycine dehydrogenase (decarboxylating), 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 Glycine dehydrogenase (decarboxylating), 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 Glycine dehydrogenase (decarboxylating), 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.
Glycine dehydrogenase (decarboxylating), mitochondrial
partner:
Reaxense
upacc:
P23378
UPID:
GCSP_HUMAN
Alternative names:
Glycine cleavage system P protein; Glycine decarboxylase; Glycine dehydrogenase (aminomethyl-transferring)
Alternative UPACC:
P23378; Q2M2F8
Background:
Glycine dehydrogenase (decarboxylating), mitochondrial, also known as Glycine cleavage system P protein, plays a pivotal role in the glycine cleavage system. It catalyzes the degradation of glycine, binding the alpha-amino group through its pyridoxal phosphate cofactor, facilitating CO(2) release and transfer of the methylamine moiety to the lipoamide cofactor of the H protein.
Therapeutic significance:
The protein is directly linked to Non-ketotic hyperglycinemia, an autosomal recessive disease characterized by severe neurological symptoms due to glycine accumulation. Understanding its mechanism offers a pathway to targeted treatments for this condition.
