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 Immunoglobulin heavy constant gamma 1 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 Immunoglobulin heavy constant gamma 1 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 Immunoglobulin heavy constant gamma 1, 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 Immunoglobulin heavy constant gamma 1. 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 Immunoglobulin heavy constant gamma 1. 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 Immunoglobulin heavy constant gamma 1 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.
Immunoglobulin heavy constant gamma 1
partner:
Reaxense
upacc:
P01857
UPID:
IGHG1_HUMAN
Alternative names:
Ig gamma-1 chain C region; Ig gamma-1 chain C region EU; Ig gamma-1 chain C region KOL; Ig gamma-1 chain C region NIE
Alternative UPACC:
P01857; A0A0A0MS08
Background:
The Immunoglobulin heavy constant gamma 1 (IGHG1) protein, known by alternative names such as Ig gamma-1 chain C region, plays a pivotal role in the immune response. It is a crucial component of immunoglobulins or antibodies, produced by B lymphocytes. These antibodies have a unique ability to bind specific antigens, triggering immune reactions that target and eliminate the antigens. The variable domains of these antibodies undergo a sophisticated process of V-(D)-J rearrangement, allowing for a highly specific immune response.
Therapeutic significance:
IGHG1's involvement in multiple myeloma, a malignant tumor of plasma cells, underscores its therapeutic significance. The disease is characterized by skeletal system involvement, hyperglobulinemia, and renal failure, among other symptoms. Genetic aberrations affecting IGHG1, such as chromosomal translocations, are pivotal in the disease's pathogenesis. Understanding the role of IGHG1 could open doors to potential therapeutic strategies targeting these genetic anomalies.
