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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor 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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor 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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor, 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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor. 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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor. 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 Lipopolysaccharide-induced tumor necrosis factor-alpha factor 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.
Lipopolysaccharide-induced tumor necrosis factor-alpha factor
partner:
Reaxense
upacc:
Q99732
UPID:
LITAF_HUMAN
Alternative names:
Small integral membrane protein of lysosome/late endosome; p53-induced gene 7 protein
Alternative UPACC:
Q99732; D3DUG1; G5E9K0; Q05DW0; Q9C0L6
Background:
The Lipopolysaccharide-induced tumor necrosis factor-alpha factor, also known as Small integral membrane protein of lysosome/late endosome or p53-induced gene 7 protein, plays a crucial role in endosomal protein trafficking and lysosomal degradation. It targets endocytosed EGFR and ERGG3 for lysosomal degradation, regulating downstream signaling cascades. Additionally, it facilitates the recruitment of ESCRT complex components to cytoplasmic membranes and interacts with NEDD4 to regulate protein degradation. This protein also contributes to gene expression regulation in the nucleus and may bind DNA, playing a role in cytokine expression regulation.
Therapeutic significance:
Given its involvement in Charcot-Marie-Tooth disease, demyelinating, 1C, understanding the role of Lipopolysaccharide-induced tumor necrosis factor-alpha factor could open doors to potential therapeutic strategies for this peripheral nervous system disorder. Its function in protein trafficking and degradation pathways offers a promising target for developing treatments aimed at modulating these processes in disease contexts.
