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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. 

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. 

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. 

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. 

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) 

The raw data for the associated manuscript is organized here into three categories: 1) relating to the measurement and analysis of the native recluse spiders loop junctions, 2) raw images found in the figures throughout the manuscript, and 3) relating to the experiments testing the effect that junction angle has on the strength of two intersecting tapes. It is recommended to browse the data files in Tree mode, which will make the files appear in folders reflecting this organization. 1) Loxosceles Loop Junction Images and Analysis The folder titled, SEM Raw Images, has all of the scanning electron microscopy (SEM) images taken of the native recluse loop junctions. Some images are close-ups of individual junctions and others take a broader perspective (macro) of many loop junctions in series. Where possible several close-up images of the individual junctions are accompanied with a macro image. These images were imported into ImageJ where the junction angle was measured. The measurements for all 41 loop junctions observed are in the folder titled, Raw Data Files in the file titled, Loxosceles Loop Junction Angle Measurements.txt. The folder titled, Raw Data Files contains, in addition to the angle measurements, the raw data for analyzing the strength of individual loop junctions. The data is in native MATLAB data format. These datasets include the complete tensile data and the cross-sectional area data for each spiders silk. The MATLAB code titled, Figure_2A_2B_code, processes the raw tensile data from the natural recluse spiders loop junctions. This data is plotted as two representative curves in Figure 2A and as a complete set as a histogram in Figure 2B. The MATLAB code titled, Figure_7_code, processes and plots the loop junction data found in, Loxosceles Loop Junction Angle Measurements.txt and executed the model of a random set of recluse loops. This code can be executed to generate Figure 7. The folder titled, Raw Data Files, must be open in MATLAB to run this code! This code uses the MATLAB function, areacalculation, to calculate the junction area for a given junction angle. 2) Raw Images This folder is organized by the respective figure in the manuscript where each image can be found. Additional metadata for each image can be found accompanying each image. 3) Tensile Data and Analysis This folder contains all of the raw tensile data for all tape-tape junction experiments conducted. All of the tensile data is in the folder titled, Raw Data Test Files. Within this folder is a .txt file for each sample tested. The file names are critical to the figure codes working properly because they contain the information for the junction angle and iterations. The file names are in the format year-month-day_trialnumber_junctionangle.txt. Also in the Raw Data Test Files folder are two functions used within some of the figure codes: fbfill and areacalculation. These functions will be used in the figure codes to properly analyze the data. To generate any figure using the MATLAB code in this folder, first open the code in MATLAB. Then within MATLAB, open the folder Raw Data Test Files. Only with this folder open in MATLAB will the code be able to find the correct raw data .txt files. The rest of the contents of this folder are MATLAB codes for specific figures in the manuscript. The only exception to this is the code titled, surfaceenergy_code, which is executed to calculate the phenomenological surface energy for the tapes used in these experiments. 

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. 

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.