MoDPA: investigating diseases through modification-dependent protein associations

Both protein-protein associations and Post-Translational Modifications (PTMs) are crucial for regulating various biological processes, but the relationship between these two regulatory mechanisms is not fully understood. To bridge this gap, I am currently developing MoDPA (Modification-dependent Protein Associations), a computational approach that aims to identify functional associations between PTMs.

Co-expression analysis is a widely utilized method for exploring the function and regulation of genes. Various techniques exist for assessing the relationship between two proteins, with the simplest being based on co-occurrence. My research project seeks to employ a similar approach to identify consistently co-occurring clusters of PTMs across experiments.

To obtain PTM identifications, public datasets from the online PRIDE repository were uniformly reprocessed using ionbot, a machine learning-based peptide search engine.

To tackle the high dimensionality and sparsity of PTM data, I employed a Variational Autoencoder (VAE) deep-learning model to compress the dataset into a low-dimensional latent space while retaining its essential features. An adjacency matrix was then constructed by calculating correlations between PTMs in the latent space, resulting in a network of co-occurring PTMs. Each node in the network represents a modification on a specific protein residue, and two modifications are connected by an edge if they were consistently co-identified in the analyzed mass spectrometry data.

A clustering analysis revealed that proteins within related pathways tend to bear similar PTMs. For instance, proteins involved in phagocytosis display co-occurring Lysine and Arginine methylation events, whereas proteins related to translation displayed co-occurring Lysine acetylation events. Intriguingly, clusters containing a combination of acetylation, phosphorylation and methylation also emerged from the analysis, offering an opportunity to study the cross-talk between these modifications.

The PTM association network, generated by MoDPA and annotated with biological information, will be made public and will serve as a source of novel hypotheses for researchers in life sciences.