Abstract
Elastic Networks (ENs) are a subset of coarse-grained (CG) models that have proven useful when studying the fluctuations of proteins. All CG models involve two aspects: (1) the mapping of the high resolution particles to low resolution particles and (2) the new interaction between the low resolution particles. The most common choice of mapping is to simply map all the particles of an amino acid to a CG bead at the position of the Cα atom (or to CG beads at both the Cα and Cβ atoms). Clearly, the interactions between the CG degrees of freedom affect the ability of the EN model to reproduce the fluctuations of the actual protein, but what about the choice of mapping? In this project, we use a method for the systematic creation of a low resolution EN from a high resolution model to investigate how the mapping can affect the ability of the EN to emulate the high resolution model. We show that the mapping has a large effect on the final accuracy and that it is possible to efficiently search for superior mappings. To this end, we present an easily calculated metric that correlates with the validity of the EN, and discuss a couple of optimization techniques that have shown success.
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