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 TRAF-interacting protein with FHA domain-containing protein A 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 TRAF-interacting protein with FHA domain-containing protein A 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 TRAF-interacting protein with FHA domain-containing protein A, 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 TRAF-interacting protein with FHA domain-containing protein A. 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 TRAF-interacting protein with FHA domain-containing protein A. 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 TRAF-interacting protein with FHA domain-containing protein A 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.
TRAF-interacting protein with FHA domain-containing protein A
partner:
Reaxense
upacc:
Q96CG3
UPID:
TIFA_HUMAN
Alternative names:
Putative MAPK-activating protein PM14; Putative NF-kappa-B-activating protein 20; TRAF2-binding protein
Alternative UPACC:
Q96CG3
Background:
TRAF-interacting protein with FHA domain-containing protein A, also known as Putative MAPK-activating protein PM14 and Putative NF-kappa-B-activating protein 20, plays a pivotal role in the activation of pro-inflammatory NF-kappa-B signaling. This activation occurs upon the detection of bacterial pathogen-associated molecular pattern metabolites (PAMPs), leading to an innate immune response. The protein promotes the oligomerization and polyubiquitination of TRAF6, activating TAK1 and IKK through a proteasome-independent mechanism.
Therapeutic significance:
Understanding the role of TRAF-interacting protein with FHA domain-containing protein A could open doors to potential therapeutic strategies.