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Enzyme Turnover Number Prediction Based on Protein 3D Structures

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2014))

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Abstract

Protein function prediction has long been a widely discussed task in the field of synthetic biology, and it is of paramount importance for gaining a deeper understanding of the roles and interactions of proteins within living organisms. Since the 3D structure data of proteins obtained experimentally are far less in quantity than the corresponding protein sequence data, most experiments related to protein function prediction currently rely on using protein sequences as training data, although 3D protein structures contain much more information. Here, an enzyme turnover number prediction model (PSKcat) is proposed based on 3D protein structures. PSKcat takes protein PDB files as input, represents proteins using a modified pre-trained model called GearNet-Edge for 3D protein structures, and combines graph neural network to characterize the substrates involved in enzyme reactions. In order to verify the effectiveness of the model, several enzyme reaction datasets were constructed, and multiple groups of comparative experiments were conducted. The experimental results demonstrate the feasibility of using 3D protein structures for enzyme function prediction, which opens up avenues for further exploration of the applications of 3D protein structures in the future.

Y. He and Y. Wang—Contributed equally to this work and should be considered co-first authors.

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Acknowledgement

This work was supported by the National Key Technology Research and Development Program of China (2022YFA0911800).

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Correspondence to Li Cheng .

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He, Y., Wang, Y., Zhang, Y., Yang, Y., Cheng, L., Alghazzawi, D. (2024). Enzyme Turnover Number Prediction Based on Protein 3D Structures. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_15

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  • DOI: https://doi.org/10.1007/978-981-97-0903-8_15

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  • Online ISBN: 978-981-97-0903-8

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