A conserved spider silk domain acts as a molecular switch that controls fibre assembly

A huge variety of proteins are able to form fibrillar structures(1), especially at high protein concentrations. Hence, it is surprising that spider silk proteins can be stored in a soluble form at high concentrations and transformed into extremely stable fibres on demand(2,3). Silk proteins are reminiscent of amphiphilic block copolymers containing stretches of polyalanine and glycine-rich polar elements forming a repetitive core flanked by highly conserved non-repetitive amino-terminal(4,5) and carboxy-terminal(6) domains. The N-terminal domain comprises a secretion signal, but further functions remain unassigned. The C-terminal domain was implicated in the control of solubility and fibre formation(7) initiated by changes in ionic composition(8,9) and mechanical stimuli known to align the repetitive sequence elements and promote beta-sheet formation(10-14). However, despite recent structural data(15), little is known about this remarkable behaviour in molecular detail. Here we present the solution structure of the C-terminal domain of a spider dragline silk protein and provide evidence that the structural state of this domain is essential for controlled switching between the storage and assembly forms of silk proteins. In addition, the C-terminal domain also has a role in the alignment of secondary structural features formed by the repetitive elements in the backbone of spider silk proteins, which is known to be important for the mechanical properties of the fibre

Scalable inference of ordinary differential equation models of biochemical processes.

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability