Coarse-graining of protein structures for the normal mode studies

Kilho Eom, Seung Chul Baek, Jung Hee Ahn, Sungsoo Na

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


The coarse-grained structural model such as Gaussian network has played a vital role in the normal mode studies for understanding protein dynamics related to biological functions. However, for the large proteins, the Gaussian network model is computationally unfavorable for diagonalization of Hessian (stiffness) matrix for the normal mode studies. In this article, we provide the coarse-graining method, referred to as "dynamic model condensation," which enables the further coarse-graining of protein structures consisting of small number of residues. It is shown that the coarser-grained structures reconstructed by dynamic model condensation exhibit the dynamic characteristics, such as low-frequency normal modes, qualitatively comparable to original structures. This sheds light on that dynamic model condensation and may enable one to study the large protein dynamics for gaining insight into biological functions of proteins.

Original languageEnglish
Pages (from-to)1400-1410
Number of pages11
JournalJournal of Computational Chemistry
Issue number8
Publication statusPublished - 2007 Jun


  • Coarse-graining
  • Gaussian network model
  • Low-frequency normal modes
  • Normal mode analysis
  • Protein dynamics

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics


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