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Graph Signal Processing on protein residue networks helps in studying its biophysical properties

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  • Srivastava, Divyanshu
  • Bagler, Ganesh
  • Kumar, Vibhor

Abstract

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on nodes are decomposed on network’s Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

Suggested Citation

  • Srivastava, Divyanshu & Bagler, Ganesh & Kumar, Vibhor, 2023. "Graph Signal Processing on protein residue networks helps in studying its biophysical properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    • Handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001589
    DOI: 10.1016/j.physa.2023.128603
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    References listed on IDEAS

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    1. Bagler, Ganesh & Sinha, Somdatta, 2005. "Network properties of protein structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 27-33.
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