Dataset Information

Engineering the Dynamic Properties of Protein Networks through Sequence Variation.

ABSTRACT: The dynamic behavior of macromolecular networks dominates the mechanical properties of soft materials and influences biological processes at multiple length scales. In hydrogels prepared from self-assembling artificial proteins, stress relaxation and energy dissipation arise from the transient character of physical network junctions. Here we show that subtle changes in sequence can be used to program the relaxation behavior of end-linked networks of engineered coiled-coil proteins. Single-site substitutions in the coiled-coil domains caused shifts in relaxation time over 5 orders of magnitude as demonstrated by dynamic oscillatory shear rheometry and stress relaxation measurements. Networks with multiple relaxation time scales were also engineered. This work demonstrates how time-dependent mechanical responses of macromolecular materials can be encoded in genetic information.

SUBMITTER: Dooling LJ

PROVIDER: S-EPMC5126713 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Engineering the Dynamic Properties of Protein Networks through Sequence Variation.

Dooling Lawrence J LJ Tirrell David A DA

ACS central science 20161018 11

The dynamic behavior of macromolecular networks dominates the mechanical properties of soft materials and influences biological processes at multiple length scales. In hydrogels prepared from self-assembling artificial proteins, stress relaxation and energy dissipation arise from the transient character of physical network junctions. Here we show that subtle changes in sequence can be used to program the relaxation behavior of end-linked networks of engineered coiled-coil proteins. Single-site s ...[more]

PMID: 27924309

Similar Datasets

Project description:Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden dynamics within protein interaction networks.

Dataset Information

Engineering the Dynamic Properties of Protein Networks through Sequence Variation.

Publications

Engineering the Dynamic Properties of Protein Networks through Sequence Variation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets