(31a) Understanding Peptide Assembly with Coarse-Grained Models Designed By Information Theory (Invited Talk)

Peptide self-assembly is a major feature of both protein aggregation and emerging routes to synthetic nanostructured biomaterials. However, this inherently multiscale process remains challenging to model – and to predict as a function of amino acid sequence – due to the large associated length and time scales. Here, we discuss a novel modeling coarse-grained modeling approach to such problems based on information theory. A quantity called the relative entropy measures the information lost upon coarse graining and hence the (inverse) fitness of a coarse-grained model. Minimization of the relative entropy provides a universal principle for coarse-graining, and a way to “automatically” discover and design coarse models of many systems. This approach enables us to develop simple but surprisingly accurate coarse peptide models, and permits large-scale simulations of several kinds of self-assembly, including amyloid-like oligomerization and aggregation, and interactions of tethered peptides on surfaces.