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 Histone acetyltransferase KAT6A 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 Histone acetyltransferase KAT6A 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 Histone acetyltransferase KAT6A, 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 Histone acetyltransferase KAT6A. 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 Histone acetyltransferase KAT6A. 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 Histone acetyltransferase KAT6A 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.
Histone acetyltransferase KAT6A
partner:
Reaxense
upacc:
Q92794
UPID:
KAT6A_HUMAN
Alternative names:
MOZ, YBF2/SAS3, SAS2 and TIP60 protein 3; Monocytic leukemia zinc finger protein; Runt-related transcription factor-binding protein 2; Zinc finger protein 220
Alternative UPACC:
Q92794; Q76L81
Background:
Histone acetyltransferase KAT6A, also known as MOZ, YBF2/SAS3, SAS2, and TIP60 protein 3, plays a pivotal role in chromatin remodeling and gene expression regulation. It acetylates lysine residues in histone H3 and H4, contributing to the transcriptional activation of genes. KAT6A is a component of the MOZ/MORF complex, enhancing its histone H3 acetyltransferase activity. It also serves as a transcriptional coactivator for RUNX1 and RUNX2 and modifies p53/TP53, influencing its transcriptional activity.
Therapeutic significance:
KAT6A's mutation is linked to Arboleda-Tham syndrome, characterized by intellectual disability and delayed development. Understanding KAT6A's role could unveil new therapeutic strategies for this genetic disorder.
