Exploring the sequence-function space of microbial fucosidases
Microbial α-L-fucosidases catalyse the hydrolysis of terminal α-L-fucosidic linkages and can perform transglycosylation reactions. Based on sequence identity, α-L-fucosidases are classified in glycoside hydrolases (GHs) families of the carbohydrate-active enzyme database. Here we explored the sequen...
| Autores: | , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/376208 |
| Acceso en línea: | http://hdl.handle.net/10261/376208 https://api.elsevier.com/content/abstract/scopus_id/85196319234 |
| Access Level: | acceso abierto |
| Palabra clave: | Cheminformatics Enzyme mechanisms Hydrolases Solution-state NMR X-ray crystallography |
| Sumario: | Microbial α-L-fucosidases catalyse the hydrolysis of terminal α-L-fucosidic linkages and can perform transglycosylation reactions. Based on sequence identity, α-L-fucosidases are classified in glycoside hydrolases (GHs) families of the carbohydrate-active enzyme database. Here we explored the sequence-function space of GH29 fucosidases. Based on sequence similarity network (SSN) analyses, 15 GH29 α-L-fucosidases were selected for functional characterisation. HPAEC-PAD and LC-FD-MS/MS analyses revealed substrate and linkage specificities for α1,2, α1,3, α1,4 and α1,6 linked fucosylated oligosaccharides and glycoconjugates, consistent with their SSN clustering. The structural basis for the substrate specificity of GH29 fucosidase from Bifidobacterium asteroides towards α1,6 linkages and FA2G2 N-glycan was determined by X-ray crystallography and STD NMR. The capacity of GH29 fucosidases to carry out transfucosylation reactions with GlcNAc and 3FN as acceptors was evaluated by TLC combined with ESI-MS and NMR. These experimental data supported the use of SSN to further explore the GH29 sequence-function space through machine-learning models. Our lightweight protein language models could accurately allocate test sequences in their respective SSN clusters and assign 34,258 non-redundant GH29 sequences into SSN clusters. It is expected that the combination of these computational approaches will be used in the future for the identification of novel GHs with desired specificities. |
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