DeepREx-WS: A web server for characterising protein-solvent interaction starting from sequence

Protein-solvent interaction provides important features for protein surface engineering when the struc-ture is absent or partially solved. Presently, we can integrate the notion of solvent exposed/buried resi-dues with that of their flexibility and intrinsic disorder to highlight regions where mutations may increase or decrease protein stability in order to modify proteins for biotechnological reasons, while pre -serving their functional integrity. Here we describe a web server, which provides the unique possibility of integrating knowledge of solvent and non-solvent exposure with that of residue conservation, flexibility and disorder of a protein sequence, for a better understanding of which regions are relevant for protein integrity. The core of the webserver is DeepREx, a novel deep learning-based tool that classifies each resi-due in the sequence as buried or exposed. DeepREx is trained on a high-quality, non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric protein chains and benchmarked on a blind test set including 200 protein sequences unrelated with the training set. Results show that DeepREx performs at the state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements the pre-dictions of DeepREx with features that allow a better characterisation of exposed and buried regions: i) residue conservation derived from multiple sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility computed with MEDUSA; iv) a predictor of secondary structure; v) the presence of dis -ordered regions as derived from MobiDB-Lite3.0. The web server allows browsing, selecting and inter-secting the different features. We demonstrate a possible application of the DeepREx-WS for assisting the identification of residues to be variated in protein surface engineering processes. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Automated summary

This study introduces a web server that integrates knowledge of solvent and non-solvent exposure with residue conservation, flexibility, and disorder to better understand relevant regions for protein integrity. DeepREx, a deep learning-based tool, classifies residues as buried or exposed and performs at the state-of-the-art in the field. The web server, DeepREx-WS, supplements DeepREx predictions with additional features for characterizing exposed and buried regions, aiding in the identification of residues for variation in protein surface engineering.