Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function

Nixon Raj, Timothy Click, Haw Yang, Jhih Wei Chu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 μs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.

Original languageEnglish (US)
Pages (from-to)5309-5320
Number of pages12
JournalComputational and Structural Biotechnology Journal
Volume19
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

Keywords

  • All-atom molecular dynamics simulation
  • Network theory
  • PDZ3
  • Protein dynamics
  • Rigidity graph
  • Scale-free
  • Serine protease
  • Structure-mechanics statistical learning

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