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Model Reduction of Large Proteins for Normal Mode Studies

Recently, I reported the model reduction method for large proteins for understanding large protein dynamics based on low-frequency normal modes. This work was pubslihed at Journal of Computational Chemistry (click here).

Coarse-Graining of protein structures for the normal mode studies

Abstracts 

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 may enable one to study the large protein dynamics for gaining insight into biological functions of proteins. 

Key words:        coarse-graining; normal mode analysis; protein dynamics; low-frequency normal modes; Gaussian network model 

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