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Abstract Nanofibrils play a pivotal role in spider silk and are responsible for many of the impressive properties of this unique natural material. However, little is known about the internal structure of these protein fibrils. We carry out polarized Raman and polarized Fourier-transform infrared spectroscopies on native spider silk nanofibrils and determine the concentrations of six distinct protein secondary structures, including β-sheets, and two types of helical structures, for which we also determine orientation distributions. Our advancements in peak assignments are in full agreement with the published silk vibrational spectroscopy literature. We further corroborate our findings with X-ray diffraction and magic-angle spinning nuclear magnetic resonance experiments. Based on the latter and on polypeptide Raman spectra, we assess the role of key amino acids in different secondary structures. For the recluse spider we develop a highly detailed structural model, featuring seven levels of structural hierarchy. The approaches we develop are directly applicable to other proteinaceous materials. 

Abstract Biomaterials with outstanding mechanical properties, including spider silk, wood, and cartilage, often feature an oriented nanofibrillar structure. The orientation of nanofibrils gives rise to a significant mechanical anisotropy, which is extremely challenging to characterize, especially for microscopically small or inhomogeneous samples. Here, a technique utilizing atomic force microscope indentation at multiple points combined with finite element analysis to sample the mechanical anisotropy of a thin film in a microscopically small area is reported. The system studied here is the tape‐like silk of the Chilean recluse spider, which entirely consists of strictly oriented nanofibrils giving rise to a large mechanical anisotropy. The most detailed directional nanoscale structure–property characterization of spider silk to date is presented, revealing the tensile and transverse elastic moduli as 9 and 1 GPa, respectively, and the binding strength between silk nanofibrils as 159±13 MPa. Furthermore, based on this binding strength, the nanofibrils’ surface energy is derived as 37 mJ m⁻², and concludes that van der Waals forces play a decisive role in interfibrillar binding. Due to its versatility, this technique has many potential applications, including early disease diagnostics, as underlying pathological conditions can alter the local mechanical properties of tissues. 

While spider silk threads mainly consist of a core of partially crystalline silk proteins, it has been found that they also exhibit a very thin skin layer of distinct structure and a coating rich in lipids and glycoproteins. These outer layers are poorly researched, but can be assumed to be a major player governing the interaction of cells with spider silk threads, as observed in cell culture. Here we propose SAXS/WAXS mapping with ultra-high spatial resolution to examine the surface layer of thin cryo-cut sections of different spider silks that have shown different cell guiding behavior in cell culture. This approach allows studying surface layers from two orientations (along and normal to fiber axis) and the cryo-approach minimizes morphological changes. In a recent nano-SAXS/WAXS beamtime at ID13, we obtained very promising data, however with whole threads and with lower resolution. This follow-up work aims to characterize the surface layer systematically. 

Peripheral nerve reconstruction through the employment of nerve guidance conduits with Trichonephila dragline silk as a luminal filling has emerged as an outstanding preclinical alternative to avoid nerve autografts. Yet, it remains unknown whether the outcome is similar for silk fibers harvested from other spider species. This study compares the regenerative potential of dragline silk from two orb‐weaving spiders, Trichonephila naurata and Nuctenea umbratica, as well as the silk of the jumping spider Phidippus regius. Proliferation, migration, and transcriptomic state of Schwann cells seeded on these silks are investigated. In addition, fiber morphology, primary protein structure, and mechanical properties are studied. The results demonstrate that the increased velocity of Schwann cells on Phidippus regius fibers can be primarily attributed to the interplay between the silk's primary protein structure and its mechanical properties. Furthermore, the capacity of silk fibers to trigger cells toward a gene expression profile of a myelinating Schwann cell phenotype is shown. The findings for the first time allow an in‐depth comparison of the specific cellular response to various native spider silks and a correlation with the fibers’ material properties. This knowledge is essential to open up possibilities for targeted manufacturing of synthetic nervous tissue replacement. 

Spider silk is biocompatible, biodegradable, and rivals some of the best synthetic materials in terms of strength and toughness. Despite extensive research, comprehensive experimental evidence of the formation and morphology of its internal structure is still limited and controversially discussed. Here, we report the complete mechanical decomposition of natural silk fibers from the golden silk orb-weaver Trichonephila clavipes into ≈10 nm-diameter nanofibrils, the material's apparent fundamental building blocks. Furthermore, we produced nanofibrils of virtually identical morphology by triggering an intrinsic self-assembly mechanism of the silk proteins. Independent physico-chemical fibrillation triggers were revealed, enabling fiber assembly from stored precursors “at-will”. This knowledge furthers the understanding of this exceptional material's fundamentals, and ultimately, leads toward the realization of silk-based high-performance materials. 

Atomic force microscopy (AFM) image raw data, force spectroscopy raw data, data analysis/data plotting, and force modeling. File Formats The raw files of the AFM imaging scans of the colloidal probe surface are provided in NT-MDTs proprietary .mdt file format, which can be opened using the Gwyddion software package. Gwyddion has been released under the GNU public software license GPLv3 and can be downloaded free of charge at http://gwyddion.net/. The processed image files are included in Gwyddions .gwy file format. Force spectroscopy raw files are also provided in .mdt file format, which can be opened using NT-MDTs NOVA Px software (we used 3.2.5 rev. 10881). All the force data were converted to ASCII files (*.txt) using the NOVA Px software to also provide them in human readable form with this data set. The MATLAB codes used for force curve processing and data analysis are given as *.m files and can be opened by MATLAB (https://www.mathworks.com/products/matlab) or by a text editor. The raw and processed force curve data and other values used for data processing are stored in binary form in *.mat MATLAB data files, which can be opened by MATLAB. Organized by figure, all the raw and processed force curve data are given in Excel worksheets (*.xlsx), one per probe/substrate combination. Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Codes for Force Curve Processing The three MATLAB codes used for force curve processing are contained in this folder. The text file Read me.txt provides all the instructions to process raw force data using these three MATLAB codes. Figure 3B, 3C – AFM images The raw (.mdt) and processed (.gwy) AFM images of the colloidal probe before and after coating with graphene oxide (GO) are contained in this folder. Figure 4 – Force Curve GO The raw data of the force curve shown in Figure 4 and the substrate force curve data (used to find inverse optical lever sensitivity) are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in the variables_GO_Tip 18.mat and GO_Tip 18.xlsx files. The force curve processing codes and instructions can be found in the Codes for Force Curve Processing folder, as mentioned above. Figure 5A – Force–Displacement Curves GO, rGO1, rGO10 All the raw data of the force curves (GO, rGO1, rGO10) shown in Figure 5A and the corresponding substrate force curve data (used to find inverse optical lever sensitivity) are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 5B, 5C – Averages of Force and Displacement for Snap-On and Pull-Off Events All the raw data of the force curves (GO, rGO1, rGO10) for all the probes and corresponding substrate force curve data are given as .mdt files and were exported as ASCII files given in this folder. The raw and processed force curve data are also provided in *.mat and *.xlsx files. The snap-on force, snap-on displacement, and pull-off displacement values were obtained from each force curve and averaged as in Code_Figure5B_5C.m. The same code was used for plotting the average values. Figure 6A – Force–Distance Curves GO, rGO1, rGO10 The raw data provided in Figure 5A – Force Displacement Curves GO, rGO1, rGO10 folder were processed into force-vs-distance curves. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 6B – Average Snap-On and Pull-Off Distances The same raw data provided in Figure 5B, 5C – Average Snap on Force, Displacement, Pull off Displacement folder were processed into force-vs-distance curves. The raw and processed force curve data of GO, rGO1, rGO10 of all the probes are also given in *.mat and *.xlsx files. The snap-on distance and pull-off distance values were obtained from each force curve and averaged as in Code_Figure6B.m. The code used for plotting is also given in the same text file. Figure 6C – Contact Angles Advancing and receding contact angles were calculated using each processed force-vs-distance curve and averaged according to the reduction time. The obtained values and the code used to plot is given in Code_Figure6C.m. Figure 9A – Force Curve Repetition The raw data of all five force curves and the substrate force curve data are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 9B – Repulsive Force Comparison The data of the zoomed-in region of Figure 9A was plotted as Experimental curve. Initial baseline correction was done using the MATLAB code bc.m, and the procedure is given in the Read Me.txt text file. All the raw and processed data are given in rGO10_Tip19_Trial1.xlsx and variables_rGO10_Tip 19.mat files. The MATLAB code used to model other forces and plot all the curves in Figure 9B is given in Exp_vdW_EDL.m. 

Adhesive tapes are versatile and widely used yet lack adhesion strength due to their tendency to fail via peeling, a weak failure mode. A tape with surprising adhesive properties is the recluse spider's 50 nm-thin silk ribbon with a 1 : 150 aspect ratio. Junctions of these microscopic sticky tapes can withstand the material's tensile failure stress of ≈1 GPa. We modeled these natural tape–tape junctions and revealed a bi-modal failure behavior, critically dependent on the two tapes’ intersection angle. One mode leads to regular, low-strength peeling failure, while the other causes the junction to self-strengthen, eliminating the inherent weakness in peeling. This self-strengthening mechanism locks the two tapes together, increasing the junction strength by 550% and allowing some junctions to remain intact after tensile failure. This impressive adhesive strength of tapes has never before been observed or predicted. We found that recluse spiders make tape junctions with pre-stress to force the locked, high-strength failure mode. We used this approach to make junctions with synthetic adhesive tapes that overcame the weak peeling failure. 

This dataset contains raw data, processed data, and the codes used for data processing in our manuscript from our Fourier-transform infrared (FTIR) spectroscopy, Nuclear magnetic resonance (NMR), Raman spectroscopy, and X-ray diffraction (XRD) experiments. The data and codes for the fits of our unpolarized Raman spectra to polypeptide spectra is also included. The following explains the folder structure of the data provided in this dataset, which is also explained in the file ReadMe.txt. Browsing the data in Tree view is recommended. Folder contents Codes Raman Data Processing: The MATLAB script file RamanDecomposition.m contains the code to decompose the sub-peaks across different polarized Raman spectra (XX, XZ, ZX, ZZ, and YY), considering a set of pre-determined restrictions. The helper functions used in RamanDecomposition.m are included in the Helpers folder. RamanDecomposition.pdf is a PDF printout of the MATLAB code and output. P Value Simulation: 31_helix.ipynb and a_helix.ipynb: These two Jupyter Notebook files contain the intrinsic P value simulation for the 31-helix and alpha-helix structures. The simulation results were used to prepare Supplementary Table 4. See more details in the comments contained. Vector.py, Atom.py, Amino.py, and Helpers.py: These python files contains the class definitions used in 31_helix.ipynb and a_helix.ipynb. See more details in the comments contained. FTIR FTIR Raw Transmission.opj: This Origin data file contains the raw transmission data measured on single silk strand and used for FTIR spectra analysis. FTIR Deconvoluted Oscillators.opj: This Origin data file was generated from the data contained in the previous file using W-VASE software from J. A. Woollam, Inc. FTIR Unpolarized MultiStrand Raw Transmission.opj: This Origin data file contains the raw transmission data measured on multiple silk strands. The datasets contained in the first two files above were used to plot Figure 2a-b and the FTIR data points in Figure 4a, and Supplementary Figure 6. The datasets contained in the third file above were used to plot Supplementary Figure 3a. The datasets contained in the first two files above were used to plot Figure 2a-b, FTIR data points in Figure 4a, and Supplementary Figure 6. NMR Raw data files of the 13C MAS NMR spectra: ascii-spec_CP.txt: cross-polarized spectrum ascii-spec_DP.txt: direct-polarized spectrum Data is in ASCII format (comma separated values) using the following columns: Data point number Intensity Frequency [Hz] Frequency [ppm] Polypeptide Spectrum Fits MATLAB scripts (.m files) and Helpers: The MATLAB script file Raman_Fitting_Process_Part_1.m and Raman_Fitting_Process_Part_2.m contains the step-by-step instructions to perform the fitting process of our calculated unpolarized Raman spectrum, using digitized model polypeptide Raman spectra. The Helper folder contains two helper functions used by the above scripts. See the scripts for further instruction and information. Data aPA.csv, bPA.csv, GlyI.csv, GlyII.csv files: These csv files contain the digitized Raman spectra of poly-alanine, beta-alanine, poly-glycine-I, and poly-glycine-II. Raman_Exp_Data.mat: This MATLAB data file contains the processed, polarized Raman spectra obtained from our experiments. Variable freq is the wavenumber information of each collected spectrum. The variables xx, yy, zz, xz, zx represent the polarized Raman spectra collected. These variables are used to calculate the unpolarized Raman spectrum in Raman_Fitting_Process_Part_2.m. See the scripts for further instruction and information. Raman Raman Raw Data.mat: This MATLAB data file contains all the raw data used for Raman spectra analysis. All variables are of MATLAB structure data type. Each variable has fields called Freq and Raw, with Freq contains the wavenumber information of the measured spectra and Raw contains 5 measured Raman signal strengths. Variable XX, XZ, ZX, ZZ, and YY were used to plot and sub-peak analysis for Figure 2c-d, Raman data points in Figure 4a, Figure 5b, Supplementary Figure 2, and Supplementary Figure 7. Variable WideRange was used to plot and identify the peaks for Supplementary Figure 3b. X-Ray X-Ray.mat: This MATLAB data file contains the raw X-ray data used for the diffraction analysis in Supplementary Figure 5. 

Raw data of scanning electron microscopy (SEM), atomic force microscopy (AFM), force spectroscopy, data analysis and plotting, optical microscopy, and finite element simulations (FEA) for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Imported raw data to MATLAB and saved MATLAB data were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) FEA results were saved as text files, .txt files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Read me file.docx More Explanations of analysis in docx format. Figure 1 Figure 1 - panel b.jpg (5.5 MB) Optical micrograph (JPEG format) Figure 1 - panel c - AFM Raw Data.gwy (8.0 MB) AFM raw data (Gwyddion format) Figure 1 - panel e - P0_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at P0 (text format) Figure 1 - panel e - Px_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Px (text format) Figure 1 - panel e - Py_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Py (text format) Figure 1 - panel e - P0_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at P0 (text format) Figure 1 - panel e - Px_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Px (text format) Figure 1 - panel e - Py_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Py (text format) Figure 1 - panel e - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 1 - panel e - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 1 - panel e - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 1 - panel e - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Figure 1- Panel e - data.mat (6 KB) MATALB struct data file for calibrated force-distance data at all indentation points (matlab .mat format) Figure 1 - panel e - Panel_e_MatlabCode.m (6 KB) MATALB code for plotting experimental and simulated force curves in panel e (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 2 Figure 2 - panel a - MATLAB data.mat (2.6 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel b - MATLAB data.mat (2.4 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel a - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel b - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel ab - MATLABcode.m (1.0 KB) MATALB code for plotting panel a b figures (matlab .m format) Figure 2 - panel c - Degree of Anisotropy datacode.m (1.0 KB) MATALB code for plotting panel c graph (matlab .m format) Figure 3 Figure 3 - panel a - App_curve_1_raw_data.txt (35 KB) Raw force-displacement data approach curve 1 (text format) Figure 3 - panel a - App_curve_2_raw_data.txt (34 KB) Raw force-displacement data approach curve 2 (text format) Figure 3 - panel a - App_curve_3_raw_data.txt (34 KB) Raw force-displacement data approach curve 3 (text format) Figure 3 - panel a - App_curve_4_raw_data.txt (34 KB) Raw force-displacement data approach curve 4 (text format) Figure 3 - panel a - Ret_curve_1_raw_data.txt (35 KB) Raw force-displacement data of retract curve 1 (text format) Figure 3 - panel a - Ret_curve_2_raw_data.txt (35 KB) Raw force-displacement data of retract curve 2 (text format) Figure 3 - panel a - Ret_curve_3_raw_data.txt (35 KB) Raw force-displacement data of retract curve 3 (text format) Figure 3 - panel a - Simulation_raw_data-part 1.txt (43 KB) simulated force-displacement data of -part 1 (text format) Figure 3 - panel a - Simulation_raw_data-part 2.txt (43 KB) simulated force-displacement data of -part 2 (text format) Figure 3 - panel a - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 3 - panel a - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 3 - panel a - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 3 - panel a - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 3 - panel b - SEM Raw Data.tiff (9 KB) SEM raw image of broken silk membrane due to extreme indentation (.tiff format) 

Raw data of optical microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and diameter measurements of the exfoliated and self-assembled nanofibrils for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Data analysis results were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. ReadMe.md This description in Markdown format. Figure 2 - Microscopy Raw Data Figure 2 - panel a.jpg (7.2 MB) Optical micrograph (JPEG format) Figure 2 - panel b.jpg (6.1 MB) Optical micrograph (JPEG format) Figure 2 - panel c f.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel d.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel e - Exfoliated Fibrils.gwy (32.0 MB) AFM raw data (Gwyddion format) Figure 3 - AFM Raw Data Figure 3 - Panel a - Exfoliated fibrils.gwy (81.5 MB) AFM raw data (Gwyddion format) Figure 3 - Panel c - Self-assembled fibrils.gwy (24.0 MB) AFM raw data (Gwyddion format) Figure 3 - Diameter Measurements Figure 3a and Figure 3c show the AFM images of exfoliated and self-assembled nanofibrils, respectively. However, due to the AFM tip-induced broadening of lateral dimensions of small features (such as nanofibrils), the diameters of nanofibrils are generally overestimated in AFM images. Hence, the diameters of the nanofibrils were estimated as the full width at half maximum (FWHM) value of line scans taken over nanofibrils perpendicular to their axial direction. Line profiles were taken at multiple locations using Gwyddion, and the raw data were stored in MATLAB struct files (lineProfileData_Exfoliated.mat and lineProfileData_Self-Assembled.mat). These data files can be directly imported into MATLAB and will appear as “DataExf” and “DataSA” in MATLAB workspace. For instance, “DataExf.x{i}” contains the x-axis data of i-th line profile, and “DataExf.y{i}” contains the y-axis data of i-th line profile. The MATLAB codes MainCode_Exf.m and MainCode_SA.m are used to fit Gaussian curves for each line profile and calculate the FWHM. The *.m files for functions gaussian.m and createFit.m must be in the same folder as the file for the main code. The main code generates figures for each line profile containing raw line profile, related Gaussian fit, and FWHM. These FWHM values are considered as the diameters of the fibrils and stored in variables called “Exf_Dia” and “SA_Dia”. Finally, these values are plotted in a histogram and calculate the statistics such as the mean and the standard deviation. Exfoliated createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Exfoliated.mat (11.7 KB) Line profiles for exfoliated nanofibrils (MATLAB struct format) MainCode_Exf.m (1.8 KB) MATLAB code file (see above) Line profile raw data - Exfoliated Folder with all corresponding cross section raw data in ASCII format Self Assembled createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Self-Assembled.mat (9.9 KB) Line profiles for self-assembled nanofibrils (MATLAB struct format) MainCode_SA.m (1.8 KB) MATLAB code file (see above) Line profile raw data - SelfAssembled Folder with all corresponding cross section raw data in ASCII format Figure 4 - AFM Raw Data Figure 4 - Panal a.gwy (73.4 MB) AFM raw data (Gwyddion format) Figure 4 - Panel e.gwy (42.0 MB) AFM raw data (Gwyddion format)