期刊
JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 28, 期 8, 页码 1400-1410出版社
WILEY
DOI: 10.1002/jcc.20672
关键词
coarse-graining; normal mode analysis; protein dynamics; low-frequency normal modes; Gaussian network model
资金
- National Research Foundation of Korea [과C6A1802] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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. (C) 2007 Wiley Periodicals, Inc.
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