TY - JOUR
T1 - Exploring the sequence-function space of microbial fucosidases
AU - Martínez Gascueña, Ana
AU - Wu, Haiyang
AU - Wang, Rui
AU - Owen, C. David
AU - Hernando, Pedro J.
AU - Monaco, Serena
AU - Penner, Matthew
AU - Xing, Ke
AU - Le Gall, Gwenaelle
AU - Gardner, Richard
AU - Ndeh, Didier
AU - Urbanowicz, Paulina A.
AU - Spencer, Daniel I. R.
AU - Walsh, Martin
AU - Angulo, Jesus
AU - Juge, Nathalie
N1 - Funding Information: The authors gratefully acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC); this research was mostly funded by the Innovate UK Biocatalyst grant Glycoenzymes for Bioindustries (BB/M029042/) supporting NJ, HW, DN, and AMG with contribution from the BBSRC Institute Strategic Programme Gut Microbes and Health BB/R012490/1 supporting NJ. HW was also supported by Grants from National Natural Science Foundation of China (32302033) and Guangdong Basic and Applied Basic Research Foundation (2022A1515110917). RW was supported by the Talent Fund of Beijing Jiaotong University (No. 2023XKRC041), and the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project, No. 2022JBXT003). JA and SM acknowledge support of BBSRC (grant BB/P010660/1). JA was also supported by the Spanish Ministry of Science, Innovation and Universities through the grant PID2019-109395GB-I00. PH was supported by the Marie Sk\u0142odowska-Curie Actions (MSCA), as part of the Horizon 2020 programme funded by the EU Commission (Grant Agreement 814102 \u2013 Sweet Crosstalk).
Acknowledgements: We would like to thank Diamond Light Source beamlines I03, I04, and I24 for beamtime and assistance, as well as the crystallisation facility at Harwell for access and support. We also thank Meiyan Xue and Ziyang Wang for their technical support.
PY - 2024/6/18
Y1 - 2024/6/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85196319234&partnerID=8YFLogxK
U2 - 10.1038/s42004-024-01212-4
DO - 10.1038/s42004-024-01212-4
M3 - Article
AN - SCOPUS:85196319234
VL - 7
JO - Communications Chemistry
JF - Communications Chemistry
SN - 2399-3669
IS - 1
M1 - 137
ER -