Search for: All records

Abstract Single wall carbon nanotubes (SWCNTs) functionalized with (bio-)polymers such as DNA are soluble in water and sense analytes by analyte-specific changes of their intrinsic fluorescence. Such SWCNT-based (bio-)sensors translate the binding of a molecule (molecular recognition) into a measurable optical signal. This signal transduction is crucial for all types of molecular sensors to achieve high sensitivities. Although there is an increasing number of SWCNT-based sensors, there is yet no molecular understanding of the observed changes in the SWCNT’s fluorescence. Here, we report THz experiments that map changes in the local hydration of the solvated SWCNT upon binding of analytes such as the neurotransmitter dopamine or the vitamin riboflavin. The THz amplitude signal serves as a measure of the coupling of charge fluctuations in the SWCNTs to the charge density fluctuations in the hydration layer. We find a linear (inverse) correlation between changes in THz amplitude and the intensity of the change in fluorescence induced by the analytes. Simulations show that the organic corona shapes the local water, which determines the exciton dynamics. Thus, THz signals are a quantitative predictor for signal transduction strength and can be used as a guiding chemical design principle for optimizing fluorescent biosensors. 

Abstract Discovery of target‐binding molecules, such as aptamers and peptides, is usually performed with the use of high‐throughput experimental screening methods. These methods typically generate large datasets of sequences of target‐binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low‐throughput experiments or other approaches. Bioinformatics‐based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders.BinderSpaceis an open‐source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text‐based and visual output of motifs, can also provide heat maps of previously measured user‐defined functional properties for all the motif‐containing molecules. Users can also run principal component analysis (PCA) and t‐distributed stochastic neighbor embedding (t‐SNE) analyses on whole datasets and on motif‐related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t‐SNE maps. If points (sequences) in two‐dimensional maps in PCA or t‐SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use ofBinderSpaceon a dataset of oligonucleotides binding to single‐wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein.BinderSpaceis openly accessible to the public via the GitHub website:https://github.com/vukoviclab/BinderSpace.