# Unraveling biomolecular and community grammars of RNA granules via machine learning

## TL;DR

This paper uses machine learning to study RNA granules, revealing how proteins interact to form and stabilize these structures important for gene regulation and disease.

## Contribution

A novel machine learning framework identifies RNA granule proteomes and uncovers PPI community grammars critical for RNA granule integrity.

## Key findings

- Machine learning models accurately identify RNA granule proteomes across diverse in vitro conditions.
- Protein–protein interactions (PPIs) form dense clusters that stabilize RNA granule structure and function.
- PPI community grammars reveal functional subunits within RNA granules.

## Abstract

Membraneless RNA granules are essential for posttranscriptional gene regulation, influencing cellular functions and contributing to neurodegenerative diseases. However, a comprehensive understanding of their compositions and organization has been challenging due to their complex nature. In this study, we develop robust machine learning models to reliably identify RNA granule proteomes within the human proteome, capturing central RNA granule characteristics despite the heterogeneity across diverse in vitro conditions. Furthermore, we uncover protein–protein interaction (PPI) community grammars within the RNA granule proteome, highlighting PPIs as key stabilizers of RNA granule structure and function. Dense PPI clusters serve as stable “cores,” forming key functional subunits across heterogeneous RNA granules. We introduce a state-of-the-art framework for understanding RNA granule biology and underscore the critical role of PPIs in maintaining RNA granule integrity.