Coarse-grained elastic models of protein structures for understand ing their mechanics and dynamics

Kilho Eom, Gwonchan Yoon, Jae Ln Kim, Sungsoo Na

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)


An insight into mechanics and /or dynamics of protein structure is a key to understand ing the biological function of protein. For gaining insight into protein mechanics and /or protein dynamics, atomistic simulation such as molecular dynamics has been extensively employed. In spite of its accurate prediction of protein mechanics and /or protein dynamics, atomistic simulation exhibits the computational limitation for large protein complex, which performs the function through dynamics and /or mechanics in the time-scale of micro-second to second regime that is currently inaccessible with atomistic simulation. In this article, we review the current state-of-art coarse-grained modeling of large protein structures for description of the mechanics and /or dynamics of such structures. Specifically, we have considered the Gō model as well as the Elastic Network Model (ENM) for studying not only the large protein dynamics but also the protein mechanics. Further, we review the currently suggested, various types of ENMs such as REACH (Realistic Extended Algorithm via Covariance Hessian) network model, heterogeneous ENM, Minimalist Network Model, and coarsegrained ENM, and their potential in predicting the large protein dynamics and /or protein mechanics. This review suggests that current state-of-art coarse-grained network model has enabled us to gain insight into large protein dynamics or mechanics currently inaccessible with atomistic simulations.

Original languageEnglish
Pages (from-to)1210-1226
Number of pages17
JournalJournal of Computational and Theoretical Nanoscience
Issue number7
Publication statusPublished - 2010 Jul


  • Coarse-grained model
  • Elastic network model
  • Go model
  • Protein dynamics
  • Protein mechanics

ASJC Scopus subject areas

  • General Chemistry
  • General Materials Science
  • Condensed Matter Physics
  • Computational Mathematics
  • Electrical and Electronic Engineering


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