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Proteins. 2015 Jul;83(7):1273-83. doi: 10.1002/prot.24819. Epub 2015 May 23.

The performance of fine-grained and coarse-grained elastic network models and its dependence on various factors.

Na H1, Song G1,2,3.

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Department of Computer Science, Iowa State University, Ames, Iowa, 50011.
Program of Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, 50011.
L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa, 50011.


In a recent work we developed a method for deriving accurate simplified models that capture the essentials of conventional all-atom NMA and identified two best simplified models: ssNMA and eANM, both of which have a significantly higher correlation with NMA in mean square fluctuation calculations than existing elastic network models such as ANM and ANMr2, a variant of ANM that uses the inverse of the squared separation distances as spring constants. Here, we examine closely how the performance of these elastic network models depends on various factors, namely, the presence of hydrogen atoms in the model, the quality of input structures, and the effect of crystal packing. The study reveals the strengths and limitations of these models. Our results indicate that ssNMA and eANM are the best fine-grained elastic network models but their performance is sensitive to the quality of input structures. When the quality of input structures is poor, ANMr2 is a good alternative for computing mean-square fluctuations while ANM model is a good alternative for obtaining normal modes.


crystal packing; hydrogen atoms; mean-square fluctuations; normal mode analysis; ssNMA; structure quality

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