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 Protein phosphatase 1D 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 Protein phosphatase 1D 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 Protein phosphatase 1D, 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 Protein phosphatase 1D. 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 Protein phosphatase 1D. 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 Protein phosphatase 1D 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.
Protein phosphatase 1D
partner:
Reaxense
upacc:
O15297
UPID:
PPM1D_HUMAN
Alternative names:
Protein phosphatase 2C isoform delta; Protein phosphatase magnesium-dependent 1 delta; p53-induced protein phosphatase 1
Alternative UPACC:
O15297; Q53XP4; Q6P991; Q8IVR6
Background:
Protein phosphatase 1D, also known as Protein phosphatase 2C isoform delta, plays a pivotal role in cellular processes by negatively regulating p53 expression and ensuring the relief of p53-dependent cell cycle arrest. It functions by dephosphorylating key proteins such as TP53 and CHEK1, contributing to their inactivation, and mediates MAPK14 dephosphorylation. Additionally, it is crucial in maintaining genome integrity through global heterochromatin silencing.
Therapeutic significance:
Given its involvement in Jansen-de Vries syndrome, breast cancer, and ovarian cancer, Protein phosphatase 1D represents a significant target for therapeutic intervention. Understanding its regulatory mechanisms offers a promising avenue for developing treatments aimed at these conditions, highlighting the protein's potential in drug discovery and cancer therapy.
