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Curvature and Torsion of Protein Main Chain as Local Order Parameters of Protein Unfolding.

Paul GrasseinPatrice DelarueAdrien NicolaiFabrice NeiersHarold A ScheragaGia G MaisuradzePatrick Senet
Published in: The journal of physical chemistry. B (2020)
Thermal protein unfolding resembles a global (two-state) phase transition. At the local scale, protein unfolding is, however, heterogeneous and probe dependent. Here, we consider local order parameters defined by the local curvature and torsion of the protein main chain. Because chemical shifts (CS's) measured by NMR spectroscopy are extremely sensitive to the local atomic environment, CS has served as a local probe of thermal unfolding of proteins by varying the position of the atomic isotope along the amino acid sequence. The variation of the CS of each Cα atom along the sequence as a function of the temperature defines a local heat-induced denaturation curve. We demonstrate that these local heat-induced denaturation curves mirror the local protein nativeness defined by the free energy landscape of the local curvature and torsion of the protein main chain described by the Cα-Cα virtual bonds. Comparison between molecular dynamics simulations and CS data of the gpW protein demonstrates that some local native states defined by the local curvature and torsion of the main chain, mainly located in secondary structures, are coupled to each other whereas others, mainly located in flexible protein segments, are not. Consequently, CS's of some residues are faithful reporters of global protein unfolding, with heat-induced denaturation curves similar to the average global one, whereas other residues remain silent about the protein unfolded state. For the latter, the local deformation of the protein main chain, characterized by its local curvature and torsion, is not cooperatively coupled to global unfolding.
Keyphrases
  • amino acid
  • protein protein
  • molecular dynamics simulations
  • binding protein
  • machine learning
  • artificial intelligence
  • high glucose
  • big data
  • quantum dots
  • data analysis
  • single molecule