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

Divyanshu Srivastava, Ganesh Bagler and Vibhor Kumar

Physica A: Statistical Mechanics and its Applications, 2023, vol. 615, issue C

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.

Keywords: Graph signal processing; Residue interaction graph; Graph Fourier transform (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001589

DOI: 10.1016/j.physa.2023.128603

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