Experimental Insights into Conformational Ensembles of Assembled ß-Sheet Peptides.

Deciphering the conformations and interactions of peptides in their assemblies offers a basis for guiding the rational design of peptide-assembled materials. Here we report the use of scanning tunneling microscopy (STM), a single-molecule imaging method with a submolecular resolution, to distinguish 18 types of coexisting conformational substates of the ß-strand of the 8-37 segment of human islet amyloid polypeptide (hIAPP 8-37). We analyzed the pairwise peptide-peptide interactions in the hIAPP 8-37 assembly and found 82 interconformation interactions within a free energy difference of 3.40 kBT. Besides hIAPP 8-37, this STM method validates the existence of multiple conformations of other ß-sheet peptide assemblies, including mutated hIAPP 8-37 and amyloid-ß 42. Overall, the results reported in this work provide single-molecule experimental insights into the conformational ensemble and interpeptide interactions in the ß-sheet peptide assembly.

Design of a self-cleanable multilevel anticounterfeiting interface through covalent chemical modulation.

Counterfeit products have posed a significant threat to consumers safety and the global economy. To address this issue, extensive studies have been exploring the use of coatings with unclonable, microscale features for authentication purposes. However, the ease of readout, and the stability of these features against water, deposited dust, and wear, which are required for practical use, remain challenging. Here we report a novel class of chemically functionalizable coatings with a combination of a physically unclonable porous topography and distinct physiochemical properties (e.g., fluorescence, water wettability, and water adhesion) obtained through orthogonal chemical modifications (i.e., 1,4-conjugate addition reaction and Schiff-base reaction at ambient conditions). Unprecedentedly, a self-cleanable and physically unclonable coating is introduced to develop a multilevel anticounterfeiting interface. We demonstrate that the authentication of the fluorescent porous topography can be verified using deep learning. More importantly, the spatially selective chemical modifications can be read with the naked eye via underwater exposure and UV light illumination. Overall, the results reported in this work provide a facile basis for designing functional surfaces capable of independent and multilevel decryption of authenticity.