AI-ACCELERATED DRUG DISCOVERY

Vacuolar protein sorting-associated protein 35

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Vacuolar protein sorting-associated protein 35 - 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 Vacuolar protein sorting-associated protein 35 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 Vacuolar protein sorting-associated protein 35 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 Vacuolar protein sorting-associated protein 35, 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 Vacuolar protein sorting-associated protein 35. 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 Vacuolar protein sorting-associated protein 35. 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 Vacuolar protein sorting-associated protein 35 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.

Vacuolar protein sorting-associated protein 35

partner:

Reaxense

upacc:

Q96QK1

UPID:

VPS35_HUMAN

Alternative names:

Maternal-embryonic 3; Vesicle protein sorting 35

Alternative UPACC:

Q96QK1; Q561W2; Q9H016; Q9H096; Q9H4P3; Q9H8J0; Q9NRS7; Q9NVG2; Q9NX80; Q9NZK2

Background:

Vacuolar protein sorting-associated protein 35 (VPS35) is a crucial component of the retromer complex, involved in transporting proteins from endosomes to the Golgi apparatus and plasma membrane. It plays a key role in preventing the misrouting of transmembrane proteins, ensuring their proper recycling and degradation pathways. VPS35 is essential for the retrograde transport of several cargo proteins, including IGF2R and SLC11A2, and interacts with various proteins to facilitate endosomal sorting and recycling.

Therapeutic significance:

Given its pivotal role in neuronal protein sorting, VPS35 is directly implicated in Parkinson disease 17, a neurodegenerative disorder characterized by the loss of dopaminergic neurons and the presence of Lewy bodies. Understanding the role of VPS35 could open doors to potential therapeutic strategies for treating Parkinson's disease by targeting the underlying protein misrouting issues.

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