No abstract

A negatively charged poly(para-phenyleneethynylene) (PPE) forms electrostatic complexes with four positively charged antimicrobial peptides (AMP). The AMPs partially quench the fluorescence of the PPE and discriminate fourteen different bacteria in water and in human urine by pattern-based fluorescence recognition; the AMP-PPE complexes bind differentially to the components of bacterial surfaces. The bacterial species and strains form clusters according to staining properties (Gram-positive and Gram-negative) or genetic similarity (genus, species, and strain). The identification and data treatment is performed by pattern evaluation with linear discriminant analysis (LDA) of the collected fluorescence intensity data.

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.self-assembly | dipeptide hydrogels | machine learning H ydrogels that are cross-linked by three-dimensional networks of modified molecules can maintain a large amount of water without dissolving its own chemical structure, which is very similar to natural tissue. As a result of favorable biocompatibility, hydrogels have great potential in biomedical applications such as drug delivery, tissue engineering, sensing, and cell encapsulation (1-7). In the past few years, considerable attention has been directed toward the design of peptide-based hydrogels in particular, not only because of their favorable features such as easy synthesis, decoration, biodegradability, and high compatibility, but also due to their wide applications in the biological and medical fields (8)(9)(10)(11)(12)(13)(14). However, to the best of our knowledge, the prediction and design of peptide-based hydrogels is still challenging, which limits our research choices on peptide-based hydrogels (15,16). Therefore, the design strategy for hydrogels based on peptides is of great significance. Our aim is to reveal the relationship between molecular structure and hydrogel behavior, which can help us to predict and design peptide hydrogels with new chemical structures.There are approaches using molecular dynamics simulation to model the self-assembly behavior of peptides into different types of nanostructures, including nanofibers, which can subsequently form hydrogels (17)(18)(19). However, it is difficult to evaluate the actual prediction accuracy of the molecular dynamics simulation methods because only a few positive peptides were selected and synthesized to test whether they could form a hydrogel. Additionally, the current reported synthetic method on 9fluorenylmethyloxycarbonyl (Fmoc)-peptide is limited to the traditional peptide synthesis method, involving step-by-step protection and deprotection. Since a high-throughput peptide generation method is not available, our first ...

We apply two three-element arrays consisting either of different GFPs or of charged fluorescent poly(p-aryleneethynylene)s as a successful, hypothesis-free tongue that discriminates more than 30 whiskies according to their country of origin, brand, blend status, and taste. The underlying mechanism is the modulation of the fluorescence intensity of the elements of the sensor array by the different whiskies. Age, country of origin, blend status, and elements of taste were discriminated by the two very different tongues.

We present a simple array composed of an anionic and a cationic poly(para-phenyleneethynylene) (PPE), together with an electrostatic complex between the two of them. The individual PPEs and the PPE complex were employed in the sensing of white wines at pH 13; the complex was also successfully employed as a sensor element at pH 3. The sensing mechanism is fluorescence quenching. Thirteen different wines were differentiated by this chemical tongue, which consists of four elements. The fluorescence quenching is not induced by the major components of the wines. Compounds such as acids, sugars, and alcohols alone do not quench the fluorescence, but rather the colored tannins and other polyphenols contained in wine are the main quenchers. However, the major constituents of wine significantly modulate the quenching of the PPEs by the tannins.