ArticleProtein science : a publication of the Protein Society2013
A mass weighted chemical elastic network model elucidates closed form domain motions in proteins.
Article in Protein science : a publication of the Protein Society, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model.International journal of molecular sciences · 2022Article
- Computational Ways to Enhance Protein Inhibitor Design.Frontiers in molecular biosciences · 2020Article
- Fabrication and Characterization of Finite-Size DNA 2D Ring and 3D Buckyball Structures.International journal of molecular sciences · 2018Article
- Article
- Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model.PloS one · 2016Article
- Bridging between NMA and Elastic Network Models: Preserving All-Atom Accuracy in Coarse-Grained Models.PLoS computational biology · 2015Article
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8 authors.
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Abstract
An elastic network model (ENM), usually Cα coarse-grained one, has been widely used to study protein dynamics as an alternative to classical molecular dynamics simulation. This simple approach dramatically saves the computational cost, but sometimes fails to describe a feasible conformational change due to unrealistically excessive spring connections. To overcome this limitation, we propose a mass-weighted chemical elastic network model (MWCENM) in which the total mass of each residue is assumed to be concentrated on the representative alpha carbon atom and various stiffness values are precisely assigned according to the types of chemical interactions. We test MWCENM on several well-known proteins of which both closed and open conformations are available as well as three α-helix rich proteins. Their normal mode analysis reveals that MWCENM not only generates more plausible conformational changes, especially for closed forms of proteins, but also preserves protein secondary structures thus distinguishing MWCENM from traditional ENMs. In addition, MWCENM also reduces computational burden by using a more sparse stiffness matrix.
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