1. ABOUT THE DATASET -------------------- Title: Dataset for Capturing Dynamic Assembly of Nanoscale Proteins During Network Formation Creator(s): Matt D G Hughes[1], Kalila R Cook [1], Sophie Cussons [2,3], Ahmad Boroumand [1], Arwen I I Tyler [4], David Head [5], David J Brockwell [2,3], and Lorna Dougan [1,2] Organisation(s): [1] School of Physics and Astronomy, Faculty of Engineering and Physical Sciences, University of Leeds, UK [2] Astbury Centre for Structural Molecular Biology, University of Leeds, UK [3] School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, UK [4] School of Food Science and Nutrition, Faculty of Environment, University of Leeds, UK [5] School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, UK Rights-holder(s):Unless otherwise stated, Copyright 2024 University of Leeds Publication Year: 2024 Description: The structural evolution of hierarchical structures of nanoscale biomolecules is crucial for the construction of functional networks in vivo and in vitro. Despite the ubiquity of these networks, the physical mechanisms behind their formation and self-assembly remains poorly understood. Here, we use photochemically cross-linked folded protein hydrogels as a model biopolymer network system, with a combined time-resolved rheology and small-angle x-ray scattering (SAXS) approach to probe both the load-bearing structures and network architectures thereby providing a cross-length scale understanding of the network formation. Combining SAXS, rheology, and kinetic modelling, we propose a dual formation mechanism consisting of a primary formation phase, where monomeric folded proteins create the preliminary protein network scaffold; and a subsequent secondary formation phase, where both additional intra-networks crosslinks form and larger oligomers diffuse to join the preliminary network, leading to a denser more mechanically robust structure. Identifying this as the origin of the structural and mechanical properties of protein networks creates future opportunities to understand hierarchical biomechanics in vivo and develop functional, designed-for-purpose, biomaterials. Cite as: Hughes et al. (2024) [Dataset]Capturing Dynamic Assembly of Nanoscale Proteins During Network Formation. https://doi.org/10.5518/1587 Related publication: Hughes et al. Capturing Dynamic Assembly of Nanoscale Proteins During Network Formation. SMALL. Accepted (2024) Contact: Matt Hughes (phymhug@leeds.ac.uk), Lorna Dougan (L.Dougan@leeds.ac.uk) 2. TERMS OF USE --------------- Copyright University of Leeds, 2024. Unless otherwise stated, this dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 3. PROJECT AND FUNDING INFORMATION ---------------------------------- Title: MESONET: Exploiting in situ protein unfolding to understand and control mesoscopic network formation Dates: Aug 22 - Aug 27 Funding organisation: Horizon Europe Guarantee Grant no.: EP/X023524/1 4. CONTENTS ----------- All data is split between 3 folders named Rheology Data, SAXS Data and Simulation Data, which contain the raw data from experiments & simulations shown in the associated publication. Rheology Data contains a csv file of the BSA hydrogel gelation curves SAXS Data contains 2 sub-folders: 'BSA_Photochemical_Gelated' which contains dat files of the scattering curves for illuminated BSA undergoing photochemcial crosslinking labelled with the latest gelation time at which they were acquired i.e. BSA_minus_1min is all the SAXS data acquired between 0 and 1 minutes of gelation time; 'BSA_RadiationDamageCheck_NoLight' which contains dat files of scattering curves of BSA not illuminated and insteaded checkign for C-ray radiation induced effects, file named for the equivalent gelation time. Simultion Data contains an excel file of the raw percolation data where each sheet is a single reaction rate. 5. METHODS ---------- Experimental and analysis methods are outlined in the main manuscript and supplementary infomation. Details of the simulations can be found at: https://pubs.rsc.org/en/content/articlelanding/2023/sm/d3sm00111c