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 T-cell surface glycoprotein CD3 gamma chain 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 T-cell surface glycoprotein CD3 gamma chain 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 T-cell surface glycoprotein CD3 gamma chain, 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 T-cell surface glycoprotein CD3 gamma chain. 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 T-cell surface glycoprotein CD3 gamma chain. 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 T-cell surface glycoprotein CD3 gamma chain 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.
T-cell surface glycoprotein CD3 gamma chain
partner:
Reaxense
upacc:
P09693
UPID:
CD3G_HUMAN
Alternative names:
T-cell receptor T3 gamma chain
Alternative UPACC:
P09693; Q2HIZ6
Background:
The T-cell surface glycoprotein CD3 gamma chain, also known as T-cell receptor T3 gamma chain, is a crucial component of the TCR-CD3 complex on T-lymphocyte cell surfaces, playing a pivotal role in the adaptive immune response. This protein facilitates signal transduction across the cell membrane upon T-cell receptor (TCR) engagement by antigen presenting cells, through phosphorylation of its immunoreceptor tyrosine-based activation motifs (ITAMs) by protein tyrosine kinases LCK and FYN, leading to downstream signaling pathway activation.
Therapeutic significance:
Given its essential role in T-cell activation and the adaptive immune response, the T-cell surface glycoprotein CD3 gamma chain is implicated in Immunodeficiency 17, a condition with variable clinical severity affecting the immune system. Understanding the role of this protein could open doors to potential therapeutic strategies for managing this immunodeficiency and possibly other related autoimmune diseases.
