da Silva, B. M. | Search bioRxiv

bioRxiv2023

A broad-spectrum family GH13 α-glucosidase from Marinovum sp., a member of the Roseobacter clade

Glycoside hydrolases (GHs) are a diverse group of enzymes that catalyze the hydrolysis of glycosidic bonds. The Carbohydrate-Active enZymes (CAZy) classification organizes GHs into families based on sequence data and function, with fewer than 1% of the predicted proteins characterized biochemically. Consideration of genomic context can provide clues to infer possible enzyme activities for proteins of unknown function. We used the MultiGeneBLAST tool to discover a gene cluster in Marinovum sp., a member of the marine Roseobacter clade, that encodes homologues of enzymes belonging to the sulfoquinovose monooxygenase pathway for sulfosugar catabolism. This cluster lacks a gene encoding a classical family GH31 sulfoquinovosidase candidate, but which instead includes an uncharacterized family GH13 protein (MsGH13) that we hypothesized could be a non-classical sulfoquinovosidase. Surprisingly, recombinant MsGH13 lacks sulfoquinovosidase activity and is a broad spectrum -glucosidase that is active on a diverse array of -linked disaccharides, including: maltose, sucrose, nigerose, trehalose, isomaltose, and kojibiose. Using AlphaFold, a 3D model for the MsGH13 enzyme was constructed that predicted its active site shared close similarity with an -glucosidase from Halomonas sp. H11 of the same GH13 subfamily that shows narrower substrate specificity.

biochemistry↗

bioRxiv2022

epitope1D: Accurate Taxonomy-Aware B-Cell Linear Epitope Prediction

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests, and antibody production. Several computational approaches have been proposed to identify, from an antigen protein, which residues are likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could be derived. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well established CSM (Cutoff Scanning Matrix) algorithm and Organism Ontology information. Our model achieved Area Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance and outperforming state-of-the-art tools. epitope1D has been made available as a user-friendly web server interface and API at http://biosig.lab.uq.edu.au/epitope1d.

bioinformatics↗