AI-ACCELERATED DRUG DISCOVERY

Proline-serine-threonine phosphatase-interacting protein 1

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Proline-serine-threonine phosphatase-interacting protein 1 - Focused Library Design

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 Proline-serine-threonine phosphatase-interacting protein 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 Proline-serine-threonine phosphatase-interacting protein 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 Proline-serine-threonine phosphatase-interacting protein 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 Proline-serine-threonine phosphatase-interacting protein 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 Proline-serine-threonine phosphatase-interacting protein 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 Proline-serine-threonine phosphatase-interacting protein 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.

Proline-serine-threonine phosphatase-interacting protein 1

partner:

Reaxense

upacc:

O43586

UPID:

PPIP1_HUMAN

Alternative names:

CD2-binding protein 1; H-PIP

Alternative UPACC:

O43586; B5BU74; B5BUK4; O43585; O95657

Background:

Proline-serine-threonine phosphatase-interacting protein 1, also known as CD2-binding protein 1 or H-PIP, plays a crucial role in the regulation of the actin cytoskeleton. It facilitates interactions between various proteins such as ABL1, PTPN18, and WAS, promoting actin polymerization required for T-cell activation. Additionally, it is involved in innate immunity by participating in the formation of pyroptosomes, crucial for the inflammatory response.

Therapeutic significance:

The protein's involvement in PAPA syndrome, characterized by early-onset inflammation affecting skin and joints, highlights its therapeutic significance. Understanding the role of Proline-serine-threonine phosphatase-interacting protein 1 could open doors to potential therapeutic strategies for treating inflammatory diseases.

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