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 Long-chain-fatty-acid--CoA ligase 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 Long-chain-fatty-acid--CoA ligase 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 Long-chain-fatty-acid--CoA ligase 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 Long-chain-fatty-acid--CoA ligase 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 Long-chain-fatty-acid--CoA ligase 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 Long-chain-fatty-acid--CoA ligase 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.
Long-chain-fatty-acid--CoA ligase 4
partner:
Reaxense
upacc:
O60488
UPID:
ACSL4_HUMAN
Alternative names:
Arachidonate--CoA ligase; Long-chain acyl-CoA synthetase 4
Alternative UPACC:
O60488; D3DUY2; O60848; O60849; Q5JWV8
Background:
Long-chain-fatty-acid--CoA ligase 4, also known as Arachidonate--CoA ligase and Long-chain acyl-CoA synthetase 4, plays a crucial role in lipid metabolism. It catalyzes the conversion of long-chain fatty acids into their active form, acyl-CoA, facilitating both the synthesis of cellular lipids and their degradation via beta-oxidation. This enzyme shows a preference for arachidonate and eicosapentaenoate as substrates, significantly influencing the modulation of glucose-stimulated insulin secretion and prostaglandin E2 levels.
Therapeutic significance:
Long-chain-fatty-acid--CoA ligase 4 is implicated in Intellectual developmental disorder, X-linked 63, and AMME complex, diseases characterized by intellectual disability among other symptoms. Understanding the role of this protein could lead to novel therapeutic strategies targeting these X-linked disorders, potentially offering new hope for patients.
