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 Alpha-ketoglutarate-dependent dioxygenase FTO 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 Alpha-ketoglutarate-dependent dioxygenase FTO 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 Alpha-ketoglutarate-dependent dioxygenase FTO, 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 Alpha-ketoglutarate-dependent dioxygenase FTO. 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 Alpha-ketoglutarate-dependent dioxygenase FTO. 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 Alpha-ketoglutarate-dependent dioxygenase FTO 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.
Alpha-ketoglutarate-dependent dioxygenase FTO
partner:
Reaxense
upacc:
Q9C0B1
UPID:
FTO_HUMAN
Alternative names:
Fat mass and obesity-associated protein; U6 small nuclear RNA (2'-O-methyladenosine-N(6)-)-demethylase FTO; U6 small nuclear RNA N(6)-methyladenosine-demethylase FTO; mRNA (2'-O-methyladenosine-N(6)-)-demethylase FTO; mRNA N(6)-methyladenosine demethylase FTO; tRNA N1-methyl adenine demethylase FTO
Alternative UPACC:
Q9C0B1; A2RUH1; B2RNS0; Q0P676; Q7Z785
Background:
Alpha-ketoglutarate-dependent dioxygenase FTO, known for its roles in RNA demethylation and energy homeostasis, is pivotal in regulating fat mass and adipogenesis. It specifically targets N(6)-methyladenosine (m6A) in various RNA species, influencing mRNA expression and stability. This protein also plays a crucial role in the differentiation of adipocytes into brown or white fat cells, affecting body size and fat accumulation.
Therapeutic significance:
FTO's involvement in severe polymalformation syndrome and obesity highlights its potential as a therapeutic target. Understanding the role of Alpha-ketoglutarate-dependent dioxygenase FTO could open doors to potential therapeutic strategies for these conditions, especially given its regulatory role in fat mass and energy homeostasis.
