The role of hydrodynamic flow in the self-assembly of dragline spider silk proteins

Abstract

Hydrodynamic flow in the spider duct induces conformational changes in dragline spider silk proteins (spidroins) and drives their assembly, but the underlying physical mechanisms are still elusive. Here we address this challenging multiscale problem with a complementary strategy of atomistic and coarse-grained Molecular Dynamics (MD) simulations with uniform flow. The conformational changes at the molecular level were analyzed for single tethered spider silk peptides. Uniform flow leads to coiled-to-stretch transitions and pushes alanine residues into $beta$-sheet and Poly-Proline II (PPII) conformations. Coarse-grained simulations of the assembly process of multiple semi-flexible block copolymers using multi-particle collision dynamics reveal that the spidroins aggregate faster but into low-order assemblies when they are less extended. At medium-to-large peptide extensions 50%-80%), assembly slows down and becomes reversible with frequent association and dissociation events, while spidroin alignment increases and alanine repeats form ordered regions. Our work highlights the role of flow in guiding silk self-assembly into tough fibers by enhancing alignment and kinetic reversibility, a mechanism likely relevant for other proteins whose function depends on hydrodynamic flow.

Automated microscopic measurement of fibrinaloid microclots and their degradation by nattokinase, the main natto protease

“…This was then followed by immediately pipetting 15 μL of assay sample on 15-well slide 'angiogenesis' glass bottom plate used without a lid (I https://ibidi.com/chambered-coverslips/245--slide-15-well-3d-glass-bottom.html), reproduced in Figure 3, and wit shaking (cf. [142,[297][298][299][300][301][302][303][304][305][306][307][308][309]). The excitation wavelength band for ThT was set at 450 nm to 499 nm and the emissio 499 nm to 529 nm.…”

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties

“…20,21 Dissipative particle dynamics (DPD) and MD simulations are used complementarily to establish and validate relationships among experimental process parameters such as spinning solution concentration and spin speed 22,23 and to understand the impact of spidroin hydrodynamic flow in the spider duct. 24 Overall, MD simulations have served as an invaluable tool for establishing mechanistic insights into the molecular underpinnings of spider silk's superb properties and other mechanisms. 20,25 However, given the computationally intensive nature of MD calculations, establishing a rigorous relation between the primary sequence of the spider silk to the macroscopic mechanical properties has been elusive with MD and other physical models.…”

Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties

“…MD has also been used to study the impact of β-sheet nanocrystal size on mesoscale mechanical properties 20,21 . Dissipative particle dynamics (DPD) and MD simulations are used complementarily to establish and validate relationships among experimental process parameters such as spinning solution concentration and spin speed 22,23 and to understand the impact of spidroin hydrodynamic flow in the spider duct 24 . Overall, MD simulations have served as an invaluable tool for establishing mechanistic insights into the molecular underpinnings of spider silk's superb properties and other mechanisms 20,25 .…”