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 DNA (cytosine-5)-methyltransferase 3A 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 DNA (cytosine-5)-methyltransferase 3A 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 DNA (cytosine-5)-methyltransferase 3A, 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 DNA (cytosine-5)-methyltransferase 3A. 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 DNA (cytosine-5)-methyltransferase 3A. 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 DNA (cytosine-5)-methyltransferase 3A 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.
DNA (cytosine-5)-methyltransferase 3A
partner:
Reaxense
upacc:
Q9Y6K1
UPID:
DNM3A_HUMAN
Alternative names:
Cysteine methyltransferase DNMT3A; DNA methyltransferase HsaIIIA
Alternative UPACC:
Q9Y6K1; E9PEB8; Q86TE8; Q86XF5; Q8IZV0; Q8WXU9
Background:
DNA (cytosine-5)-methyltransferase 3A, also known as DNMT3A, plays a pivotal role in the epigenetic regulation of the genome through the methylation of cytosine bases in DNA. This process is crucial for the establishment of DNA methylation patterns during development, affecting gene expression, imprinting, and X-chromosome inactivation. DNMT3A's ability to modify DNA at non-CpG sites and its involvement in histone methylation highlight its multifaceted role in chromatin architecture and function.
Therapeutic significance:
Mutations in DNMT3A are linked to several diseases, including Tatton-Brown-Rahman syndrome, characterized by distinctive facial features and intellectual disability; acute myelogenous leukemia, a type of cancer affecting white blood cells; and Heyn-Sproul-Jackson syndrome, associated with dwarfism and developmental delays. Understanding the role of DNMT3A in these conditions could lead to targeted therapies, offering hope for patients affected by these genetic disorders.
