Literature DB >> 27997210

Procedure for Transferable Coarse-Grained Models of Aqueous Polysaccharides.

Jörg Sauter1, Andrea Grafmüller1.   

Abstract

We present a procedure to obtain Coarse-Grained (CG) models for aqueous polysaccharide solutions that are transferable over different degrees of polymerization and different polysaccharide concentrations based on atomistic Molecular Dynamics (MD) simulations. This is achieved by a hybrid procedure combining Boltzmann Inversion (BI) and the Multiscale Coarse-Graining (MS-CG) method. In order to overcome problems that have been previously reported with this approach, namely differences in the aggregation behavior and the end to end distance between the atomistic reference simulation and the coarse-grained simulation, we employ a separation-ansatz and explicit 1-3 and 1-4 nonbonded intramolecular interactions. This allows the use of the model for long polysaccharides. We demonstrate the transferability over both concentration and degrees of polymerization, evaluate the scope for which the coarse-grained model can be applied, and then present a scheme to extend the concentration transferability. In addition, we show that the procedure can be applied to generate a transferable implicit solvent model and demonstrate that it can be used for different atomistic force fields (FFs) as well. The procedure is then applied to derive a coarse-grained model of different hemicellulose polysaccharides. The resulting model is used to demonstrate that branching with monomer side-chains significantly increases the water uptake capacity of the molecules in comparison to linear polysaccharides which is consistent with experimental results.

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Year:  2016        PMID: 27997210     DOI: 10.1021/acs.jctc.6b00613

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  1 in total

1.  An accurate coarse-grained model for chitosan polysaccharides in aqueous solution.

Authors:  Levan Tsereteli; Andrea Grafmüller
Journal:  PLoS One       Date:  2017-07-21       Impact factor: 3.240

  1 in total

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