AI-ACCELERATED DRUG DISCOVERY

Adenylyltransferase and sulfurtransferase MOCS3

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

Adenylyltransferase and sulfurtransferase MOCS3 - 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 Adenylyltransferase and sulfurtransferase MOCS3 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 Adenylyltransferase and sulfurtransferase MOCS3 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 Adenylyltransferase and sulfurtransferase MOCS3, 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 Adenylyltransferase and sulfurtransferase MOCS3. 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 Adenylyltransferase and sulfurtransferase MOCS3. 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 Adenylyltransferase and sulfurtransferase MOCS3 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.

Adenylyltransferase and sulfurtransferase MOCS3

partner:

Reaxense

upacc:

O95396

UPID:

MOCS3_HUMAN

Alternative names:

Molybdenum cofactor synthesis protein 3; Molybdopterin synthase sulfurylase

Alternative UPACC:

O95396

Background:

Adenylyltransferase and sulfurtransferase MOCS3, also known as Molybdenum cofactor synthesis protein 3 and Molybdopterin synthase sulfurylase, is pivotal in the 2-thiolation of mcm(5)S(2)U at tRNA wobble positions of cytosolic tRNA(Lys), tRNA(Glu), and tRNA(Gln). It plays a crucial role in the biosynthesis of the molybdenum cofactor, mediating the C-terminal thiocarboxylation of sulfur carriers URM1 and MOCS2A through a complex biochemical process that involves the generation of hydrogen sulfide.

Therapeutic significance:

Understanding the role of Adenylyltransferase and sulfurtransferase MOCS3 could open doors to potential therapeutic strategies.

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