Abstract
Maximum entropy procedures for estimating coarse-grain parameters from molecular dynamics (MD) simulation data are considered within the specific context of the sequence-dependent cgDNA rigid-base model of DNA. We describe a quite general approach that exploits a maximum absolute entropy principle to fit an observed matrix of covariances subject to the constraint of only allowing a prescribed sparsity pattern of nearest-neighbor interactions in the free energy. We also allow indefinite local stiffness-matrix parameter blocks that nevertheless always generate a positive-definite model stiffness matrix. Beginning from a database of atomic-resolution MD simulations of a library of short DNA oligomers in explicit solvent, these procedures deliver a complete parameter set for the cgDNA model. Due to the intrinsic linear structure of DNA and the convergence characteristics of the MD time series data, the maximum absolute entropy parameter set yields significantly improved predictions of persistence lengths, whe...
Highlights
An important problem in molecular biology is to understand how the mechanical properties of DNA depend on the sequence of bases along its two backbones
The approach based on absolute entropy employs data from only a band about the diagonal of the estimated covariance matrix, whereas the approach based on relative entropy employs data from the entire estimated covariance matrix, and we present numerical evidence to suggest that the data that is close to the diagonal has a smaller error with respect to its assumed equilibrium or stationary value than the data that is far away
We have described a procedure for estimating the material parameters in a coarse-grain rigid-base model of DNA, with sequencedependent nearest-neighbor interactions, referred to as the cgDNA model
Summary
An important problem in molecular biology is to understand how the mechanical properties of DNA depend on the sequence of bases along its two backbones.
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