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PLoS One. 2017 Oct 11;12(10):e0185658. doi: 10.1371/journal.pone.0185658. eCollection 2017.

Normal mode-guided transition pathway generation in proteins.

Author information

1
School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
2
Department of Materials Chemistry, Nagoya University, Nagoya, Japan.
3
School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea.
4
SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Republic of Korea.

Abstract

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

PMID:
29020017
PMCID:
PMC5636086
DOI:
10.1371/journal.pone.0185658
[Indexed for MEDLINE]
Free PMC Article

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