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Phallotoxins and amatoxins are a group of prominent peptide toxins produced by the death cap mushroom Amanita phalloides. Phalloidin is a bicyclic cyclopeptide with an unusual tryptathionin thioether bridge. It is a potent stabilizer of filamentous actin and in a fluorescently labeled form widely used as a probe for actin binding. Herein, we report the enantioselective synthesis of the key amino acid (2S,4R)‐4,5‐dihydroxy‐leucine as a basis for the first total synthesis of phalloidin, which was accomplished by two different synthesis strategies. Molecular‐dynamics simulations provided insights into the conformational flexibility of peptide intermediates of different reaction strategies and showed that this flexibility is critical for the formation of atropoisomers. By simulating the intermediates, rather than the final product, molecular‐dynamics simulations will become a decisive tool in orchestrating the sequence of ring formation reactions of complex cyclic peptides.

Molecular aggregation alters the optical properties of a system as fluorescence may be activated or quenched. This is usually described within the well-established framework of H- and J-aggregates. While H-aggregates show nonfluorescent blueshifted absorption bands with respect to the isolated monomer, J-aggregates are fluorescent displaying a redshifted peak. In this publication, we employ a combined approach of experiment and theory to study the complex aggregation features and photophysical properties of diaminodicyanoquinone derivatives, which show unusual and puzzling nonfluorescent redshifted absorption bands upon aggregation. Our theoretical analysis demonstrates that stable aggregates do not account for the experimental observations. Instead, we propose an unprecedented mechanism involving metastable dimeric species formed from stable dimers to generate nonfluorescent J-aggregates. These results represent a novel kind of aggregation-induced optical effect and may have broad implications for the photophysics of dye aggregates.

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of molecular dynamics simulations, where they serve to identify relevant, low-energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient reimplementation of the common-nearest-neighbor (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customization.

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of Molecular Dynamics simulations, where they serve to identify relevant, low energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient re-implementation of the common-nearest-neighbour (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customisation.