AI-ACCELERATED DRUG DISCOVERY

Synaptonemal complex protein 3

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Synaptonemal complex protein 3 - 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 Synaptonemal complex protein 3 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 Synaptonemal complex protein 3 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 Synaptonemal complex protein 3, 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 Synaptonemal complex protein 3. 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 Synaptonemal complex protein 3. 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 Synaptonemal complex protein 3 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.

Synaptonemal complex protein 3

partner:

Reaxense

upacc:

Q8IZU3

UPID:

SYCP3_HUMAN

Alternative names:

-

Alternative UPACC:

Q8IZU3

Background:

Synaptonemal complex protein 3 plays a pivotal role in meiosis, a process critical for sexual reproduction. It is essential for the pairing of homologous chromosomes, facilitating accurate genetic recombination. This protein's function is crucial in both spermatogenesis in males and, to a lesser extent, fertility in females.

Therapeutic significance:

Mutations in Synaptonemal complex protein 3 are linked to Spermatogenic failure 4, leading to azoospermia, and recurrent pregnancy loss, highlighting its potential as a target for therapeutic interventions in infertility disorders.

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