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

https://doi.org/10.1016/j.csbj.2021.09.004Get rights and content
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Highlights

  • Statistical learning from protein dynamics unravels rigidities in interaction network.

  • Backbone and side-chain mechanical couplings exhibit scale-free network properties.

  • Graphical depiction of network rigidities captures sequence co-evolution patterns.

  • Functional sites at secondary structure peripheries are mechanical hotspots.

  • Our rigidity scores are compelling metrics for residue biological significance.

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.

Keywords

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

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