Research Data Leeds Repository

Building block aspect ratio controls assembly, architecture, and mechanics of synthetic and natural protein networks - dataset

Hughes, Matt D G and Cussons, Sophie and Hanson, Benjamin S and Cook, Kalila R and Feller, Tímea and Mahmoudi, Najet and Baker, Daniel and Ariëns, Robert and Head, David A and Brockwell, David J and Dougan, Lorna (2023) Building block aspect ratio controls assembly, architecture, and mechanics of synthetic and natural protein networks - dataset. University of Leeds. [Dataset] https://doi.org/10.5518/1344

Dataset description

Fibrous networks constructed from high aspect ratio protein building blocks are ubiquitous in nature. Despite this ubiquity the functional advantage of such building blocks over globular proteins is not understood. To answer this question, we produced hydrogel network building blocks with varying numbers of protein L domains to control the aspect ratio. Using, shear rheology and small angle neutron scattering to characterise the mechanical and structural properties of photochemically crosslinked protein L networks. Here, we show that aspect ratio is a crucial property that defines network architecture and mechanics, by shifting the formation from translationally diffusion dominated to rotationally diffusion dominated. The functional advantages of this are increased mechanical strength and the rapid assembly of homogenous networks above a critical protein concentration, crucial for in vivo biological processes such as blood clotting. In addition, manipulating aspect ratio also provides a parameter in the design of future bio-mimetic and bio-inspired materials.

Divisions:

Faculty of Engineering and Physical Sciences > School of Physics and Astronomy

Related resources:

Location	Type
https://doi.org/10.1038/s41467-023-40921-7	Publication
https://eprints.whiterose.ac.uk/203049/	Publication

License:

Creative Commons Attribution 4.0 International (CC BY 4.0)

Date deposited:

04 Jan 2024 17:22

URI:

http://archive.researchdata.leeds.ac.uk/id/eprint/1212

Additional details

Creators:

Creators	ORCID	Primary affiliation	Primary affiliation ID
Hughes, Matt D G	https://orcid.org/0000-0001-5838-7939	University of Leeds	https://ror.org/024mrxd33
Cussons, Sophie	https://orcid.org/0000-0002-9318-3859	University of Leeds	https://ror.org/024mrxd33
Hanson, Benjamin S	https://orcid.org/0000-0002-6079-4506	University of Leeds	https://ror.org/024mrxd33
Cook, Kalila R	https://orcid.org/0009-0008-5534-2813	University of Leeds	https://ror.org/024mrxd33
Feller, Tímea	https://orcid.org/0000-0002-9137-5881	University of Leeds	https://ror.org/024mrxd33
Mahmoudi, Najet		ISIS Neutron and Muon Source	https://ror.org/01t8fg661
Baker, Daniel	https://orcid.org/0000-0002-5145-3320	University of Leeds	https://ror.org/024mrxd33
Ariëns, Robert		University of Leeds	https://ror.org/024mrxd33
Head, David A	https://orcid.org/0000-0003-0216-6787	University of Leeds	/ror.org/024mrxd33
Brockwell, David J	https://orcid.org/0000-0002-0802-5937	University of Leeds	https://ror.org/024mrxd33
Dougan, Lorna	https://orcid.org/0000-0002-2620-5827	University of Leeds	https://ror.org/024mrxd33

Type of data:

Dataset

Research funders:

EPSRC

Grant numbers:

EP/ P02288X/1

Publication date:

2023

Resource language:

English

Publisher:

University of Leeds

Contact Email Address:

m.d.g.hughes@leeds.ac.uk

Last Modified:

04 Jan 2024 23:35

Files

Documentation

ReadMe.txt [3kB]

ReadMe.txt [3kB]

Data

Raw_Experimental_Data.zip [6MB]

Simulation_Data.zip [1GB]

Program

Code_availability.zip [22kB]