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The Charge Transfer Network Model for Arbitrary Proteins Complexes

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

Due to the highly complex chemical structure of biomolecules, the extensive understanding of the electronic information for proteomics can be challenging. Here, we construct a charge transfer database at residue level derived from tens of thousands of electronic structure calculations among 20 × 20 possible amino acid side-chains combinations, which are extracted from available high-quality structures of thousands of protein complexes. Then, we propose the data driven network (D2Net) procedure to quickly identify the critical residue or residue groups for any possible protein structure. As an initial evaluation, we apply this model to scrutinize the charge transfer networks for randomly selected a protein which is associated with signal transduction. This D2Net model highlights the global view of the charge transfer topology in representative proteins, for which the most critical residues show the largest number of degrees acting as network hubs. This work provides us a promising tool for efficiently understanding the electronic information in the growing number of high-quality experimental proteins structures, with minor computational costs.

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Acknowledgements

This work was financially supported by the Binzhou University (No. 2019Y13). The calculations are partially performed on the Aliyun Elastic Compute Service. The authors also thanks professor Jun Gao for his earlier support for this work. This work also receives free supported by Xiazkey Company for technological optimizations.

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Authors and Affiliations

  1. School of Information Engineering, Binzhou University, Binzhou, Shandong, China

    Fang Liu

  2. College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China

    Likai Du

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  1. Fang Liu
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  2. Likai Du
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Correspondence to Likai Du .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Liu, F., Du, L. (2023). The Charge Transfer Network Model for Arbitrary Proteins Complexes. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25190-0

  • Online ISBN: 978-3-031-25191-7

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