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1 Capsaicin-coated silver nanoparticles inhibit amyloid fibril formation of serum albumin Bibin G. Anand, Kriti Dubey, Dolat Singh Shekhawat, Karunakar Kar* Center for Biologically Inspired Systems Science, Indian Institute of Technology Jodhpur, Old Residency Road, Jodhpur, Rajasthan, India SUPPLEMENTARY INFORMATION Figure S1. Structure of the capsaicin molecule.

2 Figure S2- Silver nanoparticles capped with capsaicin showing tyndal effect.

3 Figure S3. The surface zeta potential graph for capsaicin capped silver nanoparticles showing a negative potential of -42 mv.

4 A B Figure S4. Particle size characterization of control silver nanoparticles, AgNPs (without capsaicin). (A ) Particles size distribution histograms of AgNPs nanoparticles showing an average hydrodynamic radius of ~60nm. (B) The surface zeta potential graph for control silver nanoparticles showing a negative potential of mv.

5 Wavelength (nm) A wave length SIZE y = x R² = Linear (wave length) Size (nm) B Particle size y x c m AgNps (Cap) AgNps Figure S5. Size calculation of the synthesized nanoparticles using MiePlot (Prathna et al., 2011). (A) The plot of theoretical particle size against absorption maxima. (B) Calculated particle size from the derived linear equation. Detailed methods followed for this analysis are give below.

6 Methods used for theoretical prediction of particle size using MiePlot Theoretical predictions of Surface Plasmon Resonance (SPR) peak and the particle size of the solutions were determined mathematically by using MiePlot v. 4.4 software (Prathna et al., 2011). This model theoretically determines the particle size distribution in the solution by plotting the scattering efficiency of the sample as a function of wavelength. Particles were assumed to be spherical and might be poly dispersed in water as the surrounding medium with a standard deviation of 10 % in the selected parameters of the software. The wavelength range was selected from 200 nm to 700 nm. The corresponding graph generated from this model was compared with the experimental data for further calculations. The absorption spectra of the synthesized nanoparticles exhibited single SPR peak concluding the particles were spherical. An attempt was made to derive the practical relationship correlating the theoretical particle size from the absorption maxima. This showed the relationship between the standard SPR position and the assumed particle size. With the increase in particle size there was red shift in the SPR position (increase in wavelength). SPR position with the assumed particles sizes approximated with linear function. The theoretical fitted linear function was calculated as y=5.4939x with the R 2 value of Using the above empirical linear equation, theoretical particle size was calculated ( Anand et al., 2015) the predicted particle size for AgNPs and AgNPs Cap was ~17nm, and ~30nm (Figure S6).

7 Probability (k) Probability (k) A Beta Strands 0.5 B BSA Disorderness Residue (k) Residue (k) Figure S6. Sequence analysis of BSA using PASTA. (A) Aggregation prone regions of BSA sequence. (B) Disorderness profile of amino acid residues of the BSA protein. The PDB ID of BSA considered for this study is 4F5S. The analysis of sequence was performed using the available online tool, PASTA 2.0 (Walsh et al. 2014)

8 A B Figure S7. Prediction of aggregation prone regions by AGGRESCAN 3D online tool (Zambrano et al. 2015). AGGRESCAN 3D plot showing the aggregation propensity of the residues in the A-chain (panel A) and B-chain(panel B) of BSA protein (PDB ID 4F5S). Here, the X-axis represents amino acids and Y-axis represents their respective A3D scores. The residual score above 0 are considered as aggregation prone. The residual score below 0 are meant to be soluble residues.

9 Figure S8. AGGRESCAN 3D score for the aggregation prone amino acid residues in BSA (based on the A3D scoring values). The red arrows indicate the residues that interact with capsaicin as evident from our molecular docking studies.

10 Figure S9. AGGRESCAN-3D analysis of BSA (PDB:4F5S) structure obtained from AGGRESCAN online tool. The protein surface is colored according to A3D score in gradient from red (high-predicted aggregation propensity) to white (negligible impact on protein aggregation) to blue (high-predicted solubility). References: 1. Prathna, T. C., Chandrasekaran, N., Raichur, A. M. & Mukherjee, A. Biomimetic synthesis of silver nanoparticles by Citrus limon (lemon) aqueous extract and theoretical prediction of particle size. Colloids Surf. B. Biointerfaces 82, (2011). 2. Anand, B. G., Thomas, C. K. N., Prakash, S. & Kumar, C. S. Biosynthesis of silver nano particles by marine sediment fungi for a dose dependent cytotoxicity against hep2 cell lines. Biocatal. Agric. Biotechnol. (2015). doi: /j.bcab Walsh, I., Seno, F., Tosatto, S. C. E. & Trovato, A. PASTA 2.0: an improved server for protein aggregation prediction. Nucleic Acids Res. 42, W301 7 (2014). 4. Zambrano, R. et al. AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. Nucleic Acids Res. 43, W (2015).