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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.

Computational Biology and Chemistry
|April 19, 2026
PubMed
Summary

This study identifies key factors influencing xylanase thermostability using integrated sequence and structure features. Hydrophobicity and GTPC-related features are most influential for engineering thermostable enzymes.

Keywords:
Key featureMelting temperatureOptimal catalytic temperatureThermal stabilityXylanase

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Area of Science:

  • Biochemistry and Molecular Biology
  • Enzyme Engineering
  • Computational Biology

Background:

  • Xylanase is crucial for lignocellulose degradation and industrial applications like paper and food processing.
  • Thermostable xylanases are vital for efficient industrial processes, but understanding their stability determinants is challenging.
  • Existing datasets for xylanase thermostability are scarce, hindering the development of improved enzymes.

Purpose of the Study:

  • To identify key determinants of xylanase thermostability through multi-view feature integration.
  • To provide biological insights into the mechanisms governing enzyme stability.
  • To inform the rational design and engineering of enhanced thermostable xylanases.

Main Methods:

  • Construction of comprehensive datasets (Tmxyl and Toptxyl) from experimental xylanase thermostability data.
  • Extraction of multi-view features: physicochemical sequence descriptors, learned sequence representations, and structural network features.
  • Integration of features using an attention mechanism to develop predictive models.

Main Results:

  • Developed models achieving high accuracy (0.901 and 0.911) for predicting melting and optimal catalytic temperatures.
  • Explicit sequence features were identified as the most critical determinants of thermostability.
  • GTPC-related and hydrophobicity-driven features emerged as highly influential factors.

Conclusions:

  • Multi-view feature integration significantly improves the prediction of xylanase thermostability.
  • Explicit sequence features, particularly those related to hydrophobicity and GTPC, are key targets for engineering thermostable xylanases.
  • This data-driven approach provides a foundation for rational enzyme design and development.