Temperature-induced unfolding behavior of proteins studied by tensorial elastic network model

Amit Srivastava, Rony Granek

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Motivated by single molecule experiments and recent molecular dynamics (MD) studies, we propose a simple and computationally efficient method based on a tensorial elastic network model to investigate the unfolding pathways of proteins under temperature variation. The tensorial elastic network model, which relies on the native state topology of a protein, combines the anisotropic network model, the bond bending elasticity, and the backbone twist elasticity to successfully predicts both the isotropic and anisotropic fluctuations in a manner similar to the Gaussian network model and anisotropic network model. The unfolding process is modeled by breaking the native contacts between residues one by one, and by assuming a threshold value for strain fluctuation. Using this method, we simulated the unfolding processes of four well-characterized proteins: chymotrypsin inhibitor, barnase, ubiquitein, and adenalyate kinase. We found that this step-wise process leads to two or more cooperative, first-order-like transitions between partial denaturation states. The sequence of unfolding events obtained using this method is consistent with experimental and MD studies. The results also imply that the native topology of proteins “encrypts” information regarding their unfolding process. Proteins 2016; 84:1767–1775.

Original languageEnglish
Pages (from-to)1767-1775
Number of pages9
JournalProteins: Structure, Function and Bioinformatics
Issue number12
StatePublished - 1 Dec 2016


  • biopolymers
  • bond bending
  • crack propagation
  • elastic network models
  • protein unfolding
  • twist elasticity

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology


Dive into the research topics of 'Temperature-induced unfolding behavior of proteins studied by tensorial elastic network model'. Together they form a unique fingerprint.

Cite this