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Protein Residue Networks from Energetic and Geometric Data: Are They Identical?

Vladimir SladekHiroaki TokiwaHitoshi ShimanoYasuteru Shigeta
Published in: Journal of chemical theory and computation (2018)
Protein residue networks (PRN) from energetic and geometric data are probably not identical. PRNs constructed from ab initio pair interaction energies are analyzed for the first time and compared to PRN based on center of mass separation. We use modern, previously unused algorithms such as global and local efficiencies to quantitatively confirm that both types of PRNs do exhibit small-world character. The main novelty finding is that interaction energy-based PRNs preserve small-world character even when clustered. A node hierarchy independent of the cutoff energy used for the edge creation is characteristic for them. Efficiency centrality identifies hubs responsible for such behavior. The interaction energy-based PRNs seem to comply with the scale-free network model with respect to efficiency centrality distribution as opposed to distance based PRNs. Community detection is introduced into protein network research as an extension beyond cluster analysis to study tertiary and quaternary structures.
Keyphrases
  • amino acid
  • machine learning
  • electronic health record
  • big data
  • binding protein
  • high resolution
  • mental health
  • lymph node
  • genome wide
  • deep learning
  • dna methylation
  • mass spectrometry
  • artificial intelligence