Structural Characterization of β-Xylosidase XynB2 from Geobacillus stearothermophilus CECT43: A Member of the Glycoside Hydrolase Family GH52

β-xylosidases (4-β-d-xylan xylohydrolase, E.C. 3.2.1.37) are glycoside hydrolases (GH) catalyzing the hydrolysis of (1→4)-β-d-xylans, allowing for the removal of β-d-xylose residues from its non-reducing termini. Together with other xylan-degrading enzymes, β-xylosidases are involved in the enzymatic hydrolysis of lignocellulosic biomass, making them highly valuable in the biotechnological field. Whereas different GH families are deeply characterized from a structural point of view, the GH52 family has been barely described. In this work, we report the 2.25 Å resolution structure of Geobacillus stearothermophilus CECT43 XynB2, providing the second structural characterization for this GH family. A plausible dynamic loop closing the entrance of the catalytic cleft is proposed based on the comparison of the available GH52 structures, suggesting the relevance of a dimeric structure for members of this family. The glycone specificity at the −1 site for GH52 and GH116 members is also explained by our structural studies.This research was funded by the Spanish Ministry of Science and Innovation/FEDER funds Grant PID2020-116261GB-I00/AEI/10.13039/501100011033 (JAG), by the European Regional Development Fund Andalucía 2014–2020 Grant UAL18-CTS-B032-A (FRV) and by the Own Research and Transfer Plan 2020 of the University of Almeria Grant PPUENTE2020/006 (FJLHV)

Evidential classification of incomplete data via imprecise relabelling : Application to plastic sorting

International audienceBesides ecological issues, the recycling of plastics involves economic incentives that encourage industrial firms to invest in the field. Some of them have focused on the waste sorting phase by designing optical devices able to discriminate on-line between plastic categories. To achieve both ecological and economic objectives, sorting errors must be minimized to avoid serious recycling problems and significant quality degradation of the final recycled product. Even with the most recent acquisition technologies based on spectral imaging, plastic recognition remains a tough task due to the presence of imprecision and uncertainty, e.g. variability in measurement due to atmospheric disturbances, ageing of plastics, black or dark-coloured materials etc. The enhancement of recent sorting techniques based on classification algorithms has led to quite good performance results, however the remaining errors have serious consequences for such applications. In this article, we propose an imprecise classification algorithm to minimize the sorting errors of standard classifiers when dealing with incomplete data, by both integrating the processing of classification doubt and hesitation in the decision process and improving the classification performances. To this end, we propose a relabelling procedure that enables better representation of the imprecision of the learning data, and we introduce the belief functions framework to represent the posterior probability provided by a classifier. Finally, the performances of our approach compared to existing imprecise classifiers is illustrated on the sorting problem of four plastic categories from mid-wavelength infra-red spectra acquired in an industrial context