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 AF4/FMR2 family member 4 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 AF4/FMR2 family member 4 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 AF4/FMR2 family member 4, 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 AF4/FMR2 family member 4. 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 AF4/FMR2 family member 4. 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 AF4/FMR2 family member 4 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.
AF4/FMR2 family member 4
partner:
Reaxense
upacc:
Q9UHB7
UPID:
AFF4_HUMAN
Alternative names:
ALL1-fused gene from chromosome 5q31 protein; Major CDK9 elongation factor-associated protein
Alternative UPACC:
Q9UHB7; B2RP19; B7WPD2; Q498B2; Q59FB3; Q6P592; Q8TDR1; Q9P0E4
Background:
AF4/FMR2 family member 4, also known as ALL1-fused gene from chromosome 5q31 protein, plays a pivotal role in the super elongation complex (SEC). This complex is essential for enhancing the RNA polymerase II transcription rate by mitigating transient pauses along the DNA. AFF4 serves as a scaffold within the SEC, facilitating the recruitment of ELL proteins and the P-TEFb complex. Its involvement is also noted in HIV-1 virus gene expression, prompted by the viral Tat protein.
Therapeutic significance:
AFF4's association with CHOPS syndrome, characterized by cognitive impairment, heart defects, and skeletal dysplasia, underscores its clinical relevance. Understanding the role of AFF4 could open doors to potential therapeutic strategies for this syndrome and other related conditions.
