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 2-oxoisovalerate dehydrogenase subunit alpha, 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 2-oxoisovalerate dehydrogenase subunit alpha, 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 2-oxoisovalerate dehydrogenase subunit alpha, 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 2-oxoisovalerate dehydrogenase subunit alpha, 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 2-oxoisovalerate dehydrogenase subunit alpha, 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 2-oxoisovalerate dehydrogenase subunit alpha, 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.
2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial
partner:
Reaxense
upacc:
P12694
UPID:
ODBA_HUMAN
Alternative names:
Branched-chain alpha-keto acid dehydrogenase E1 component alpha chain
Alternative UPACC:
P12694; B4DP47; E7EW46; Q16034; Q16472
Background:
The 2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial, also known as the branched-chain alpha-keto acid dehydrogenase E1 component alpha chain, plays a pivotal role in amino acid metabolism. It forms part of the BCKD complex, essential for the oxidative decarboxylation of alpha-ketoacids derived from branched-chain amino acids like valine, leucine, and isoleucine, facilitating energy production.
Therapeutic significance:
Maple syrup urine disease 1A, a metabolic disorder linked to mutations affecting this protein, underscores its critical biological function. The disease manifests with severe neurological symptoms due to the accumulation of branched-chain amino acids. Understanding the protein's role could pave the way for innovative treatments targeting the underlying genetic and metabolic pathways.
