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Multi-view identification of key features affecting xylanase thermostability
Shuyi Pan1, Lingzhi Liu1, Qunfang Yan1
1School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Abstract:
Xylanase catalyzes the hydrolysis of xylan into small-molecule saccharides including xylo-oligosaccharides, xylobiose and xylose, and acts as a key enzyme for lignocellulose degradation. Thermophilic xylanase plays a critical role in various industrial processes, such as acting as a bleaching agent in the paper industry and enhancing the quality of food and feed in food and feed processing. Exploring the underlying mechanisms governing xylanase thermostability is crucial for the successful engineering and expression of thermostable xylanases. Given the current scarcity of datasets related to xylanase thermostability and the lack of a comprehensive multi-view characterization of its determinants, this study primarily targets determinant identification and biological insight. We construct curated datasets with experimentally measured thermostability labels and identify the key factors governing xylanase thermostability by integrating complementary sequence features and structure features. To this end, we first constructed two comprehensive datasets named Tmxyl with melting temperature and Toptxyl with optimal catalytic temperature, both from xylanase wet-lab experiments. Subsequently, we extracted xylanase features from multiple perspectives, including explicit physicochemical sequence descriptors, learned sequence representations, and structural network-derived features from residue interaction graphs. By integrating these multi-view features through an attention mechanism, we developed models with optimal accuracy rates of 0.901 and 0.911 respectively, which demonstrated significantly improved performance after feature fusion. We also found that explicit sequence features emerged as the most critical determinants, with GTPC-related features identified as common factors influencing both Tm and Topt. Notably, hydrophobicity-driven features were found to be the most influential. Overall, these findings provide a multi-view, data-driven basis for prioritizing thermostability-relevant determinants and can inform the rational engineering and design of more thermostable xylanases.
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