Ionic liquids (ILs) have shown promise in controlling protein function through structural control and manipulation, but the selection of ILs for use in biological systems is complicated by the specificity of IL behavior. For example, recent work has shown that a peptide dissolved in an IL generally suffers a restriction in available conformations when compared to water, yet other conformations not observed in water can be facilitated by the IL, like cis/trans isomerization of peptide bonds along the amino acid backbone. These effects, however, depend sensitively on the particular amino acid sequence and the IL. There is currently no obvious method for selecting a suitable ionic liquid for a particular protein, because there is no comprehensive understanding of what causes each ionic liquid to induce distinct changes in different proteins. While all-atom simulations of protein/IL interactions have proven to be useful for studying specific systems in great detail, in order to achieve a high throughput characterization of the effects of ionic liquids on biomolecular systems, it is critical to develop a protocol more well-suited for adequately exploring the immense range of IL phase space. Coarse-grained models are uniquely suited in this regard. Here we present a protocol of mixed resolution molecular dynamics simulations, consisting of coarse-grained ionic liquids and atomistic proteins, which rapidly screens a large number of ILs, in order to identify ILs well suited to controlling the structure of a specific protein. Such a protocol serves to discern correlations between the physical properties of an ionic liquid and the changes in structural properties of the protein, such as secondary structure, radius of gyration, and deviation from a reference structure. Mixed resolution modeling could provide an increased understanding of the behavior of ionic liquids and an efficient framework for predicting ionic liquids well-suited for a specific purpose.

Narrowing limitless: A method for selecting ionic liquids to control protein structure

254th National Meeting (2017)

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