AI-ACCELERATED DRUG DISCOVERY

Parkinson disease protein 7

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Parkinson disease protein 7 - 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 Parkinson disease protein 7 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 Parkinson disease protein 7 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 Parkinson disease protein 7, 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 Parkinson disease protein 7. 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 Parkinson disease protein 7. 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 Parkinson disease protein 7 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.

Parkinson disease protein 7

partner:

Reaxense

upacc:

Q99497

UPID:

PARK7_HUMAN

Alternative names:

Maillard deglycase; Oncogene DJ1; Parkinsonism-associated deglycase; Protein DJ-1; Protein/nucleic acid deglycase DJ-1

Alternative UPACC:

Q99497; B2R4Z1; O14805; Q6DR95; Q7LFU2

Background:

Parkinson disease protein 7 (PARK7), also known as Protein DJ-1, plays a pivotal role in cellular defense against oxidative stress and cell death. It functions as an oxidative stress sensor, redox-sensitive chaperone, and protease. PARK7 is crucial in neuroprotective mechanisms, male fertility, cell growth, and transformation. It exhibits protein and nucleotide deglycase activity, repairing glycated proteins and nucleotides, thus preventing the formation of advanced glycation endproducts.

Therapeutic significance:

PARK7's involvement in Parkinson disease 7, a neurodegenerative disorder with symptoms like tremor and bradykinesia, underscores its therapeutic significance. Understanding PARK7's role could open doors to potential therapeutic strategies, especially considering its neuroprotective functions and involvement in oxidative stress response.

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