Datasets associated with 'Modelling Network Formation in Folded Protein Hydrogels by Cluster Aggregation Kinetics'

Datasets containing data associated with the paper “Modelling Network Formation in Folded Protein Hydrogels by Cluster Aggregation Kinetics”. Dataset 1 contains data file outputs per simulation run, Dataset 2 contains finite size test data outputs and Dataset 3 contains pore size data outputs

Trimethylamine N-oxide (TMAO) Resists The Compression of Water Structure by Magnesium Perchlorate : Terrestrial Kosmotrope vs Martian Chaotrope - dataset

The presence of magnesium perchlorate (Mg(ClO4)2) as the dominating ionic compound in the Martian regolith and the recent discovery of a subsurface lake on Mars suggests that beneath the Martian surface may lie an aqueous environment suitable for life, rich in chaotropic ions. Closer to Earth, terrestrial organisms use osmolytes, such as trimethylamine N-oxide (TMAO), to overcome the biologically damaging effects of pressure. While previous studies have revealed that Mg(ClO4)2 acts to modify water structure as if it has been pressurized, little is known about the competing effects of chaotropes and kosmotropes. Therefore the question here is whether TMAO can help to preserve the hydrogen bond network of water against the pressurising effect of Mg(ClO4)2? We address this question using neutron scattering, computational modelling using Empirical Potential Structure Refinement (EPSR) analysis, and a new approach to quantifying hydrogen bond conformations and energies. We find that the addition of 1.0 M TMAO to 0.2 M Mg(ClO4)2 or 2.7 M Mg(ClO4)2 is capable of partially restoring the hydrogen bond network of water, and the fraction of water molecules in conformations associated with hydrogen bond switching. This suggests that terrestrial protecting osmolytes could provide a protective mechanism to the extremes found in Martian environments for biological systems

Network Growth and Structural Characteristics of Globular Protein Hydrogels

Folded protein-based hydrogels are a novel class of biomaterials which combine the useful viscoelastic properties of individual proteins together with the prospect of rational design principles. Although the macroscopic properties of these materials have been well studied, there is a paucity of understanding of their mesoscopic formation mechanisms, especially given the differences in building blocks compared to biopolymer hydrogels. We present the results of a simulation study into the growth of polymeric networks of chemically cross-linked folded proteins that form the structural backbone of these hydrogels, observing how experimentally controllable parameters affect the resultant network growth and structural characteristics. We show that the initial volume fraction emerges as a dominant parameter at the network level but that the properties of the single protein remain important. We ultimately show that we can tune the properties of a monodisperse protein hydrogel network only within limits which are dictated primarily by implicit diffusion time scales

Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

Single-molecule force spectroscopy using an atomic force microscope (AFM) can be used to measure the average unfolding force of proteins in a constant velocity experiment. In combination with Monte Carlo simulations and through the application of the Zhurkov-Bell model, information about the parameters describing the underlying unfolding energy landscape of the protein can be obtained. Using this approach, we have completed protein unfolding experiments on the polyprotein (I27) 5 over a range of pulling velocities. In agreement with previous work, we find that the observed number of protein unfolding events observed in each approach-retract cycle varies between one and five, due to the nature of the interactions between the polyprotein, the AFM tip, and the substrate, and there is an unequal unfolding probability distribution. We have developed a Monte Carlo simulation that incorporates the impact of this unequal unfolding probability distribution on the median unfolding force and the calculation of the protein unfolding energy landscape parameters. These results show that while there is a significant, unequal unfolding probability distribution, the unfolding energy landscape parameters obtained from use of the Zhurkov-Bell model are not greatly affected. This result is important because it demonstrates that the minimum acceptance criteria typically used in force extension experiments are justified and do not skew the calculation of the unfolding energy landscape parameters. We further validate this approach by determining the error in the energy landscape parameters for two extreme cases, and we provide suggestions for methods that can be employed to increase the level of accuracy in single-molecule experiments using polyproteins