Marco Anteghini

The paper compares transcriptomics and proteomics data from wild-type and peroxisome-deficient yeast to identify changes in cellular processes due to peroxisome loss.

PortPred uses deep learning embeddings to identify transporter proteins and their substrates from amino acid sequences, outperforming existing methods.

This paper benchmarks deep learning-based embeddings for predicting peroxisomal protein-protein interactions using sequence data and integrates AlphaFold2-Multimer predictions for improved accuracy.

The paper introduces a web server with three algorithms for predicting sub-peroxisomal and sub-mitochondrial protein localisation and PTS1 signals, aiming to accelerate research in peroxisomal biology.

In-Pero predicts peroxisomal protein sub-localisation using deep learning embeddings, achieving over 0.9 classification accuracy.