E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence

Abstract

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

Keywords: accessible surface area; deep learning; protein language models; relative surface area; web server.