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 MAP kinase-activated protein kinase 5 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 MAP kinase-activated protein kinase 5 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 MAP kinase-activated protein kinase 5, 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 MAP kinase-activated protein kinase 5. 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 MAP kinase-activated protein kinase 5. 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 MAP kinase-activated protein kinase 5 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.
MAP kinase-activated protein kinase 5
partner:
Reaxense
upacc:
Q8IW41
UPID:
MAPK5_HUMAN
Alternative names:
p38-regulated/activated protein kinase
Alternative UPACC:
Q8IW41; B3KVA5; O60491; Q86X46; Q9BVX9; Q9UG86
Background:
MAP kinase-activated protein kinase 5, also known as p38-regulated/activated protein kinase, plays a pivotal role in mTORC1 signaling and post-transcriptional regulation. It phosphorylates several key proteins including FOXO3, ERK3/MAPK6, and p53/TP53, acting as a tumor suppressor and mediating Ras-induced senescence. Its involvement in the atypical MAPK signaling pathway underscores its complex regulatory functions in cellular processes.
Therapeutic significance:
Given its role in tumor suppression and regulation of critical signaling pathways, MAP kinase-activated protein kinase 5 holds significant therapeutic potential. Understanding its mechanisms could lead to novel interventions for Neurocardiofaciodigital syndrome, characterized by developmental delay and congenital defects, and potentially other diseases linked to dysregulated mTORC1 signaling and cellular senescence.