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The dimeric diketopiperazine (DKPs) alkaloids are a diverse family of natural products (NPs) whose unique structural architectures and biological activities have inspired the development of new synthetic methodology to access these molecules. However, catalystcontrolled methods that enable the selective formation of constitutional and stereoisomeric dimers from a single monomer are lacking. To resolve this long-standing synthetic challenge, we sought to characterize the biosynthetic enzymes that assemble these NPs for application in biocatalytic syntheses. Genome mining enabled identification of the cytochrome P450, NzeB (derived from Streptomyces sp. NRRL F-5053), which catalyzes both intermolecular carbon-carbon (C-C) and carbon-nitrogen (C-N) bond formation, generating all currently known DKP dimer scaffolds isolated from bacterial sources. To identify the molecular basis for the flexible site-, stereo-, and chemoselectivity of NzeB, we obtained high-resolution crystal structures (1.5Å) of the protein in complex with native and non-native substrates. This, to our knowledge, represents the first crystal structure of an oxidase catalyzing direct, intermolecular C-H amination. Site-directed mutagenesis was employed to assess the role individual active site residues play in guiding selective DKP dimerization. Finally, computational approaches were employed to evaluate plausible mechanisms regarding NzeB function and its ability to catalyze both CC and C-N bond formation. These results provide a structural and computational rationale for the catalytic versatility of NzeB, as well as new insights into variables that control selectivity of CYP450 diketopiperazine dimerases. ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

C-H functionalization represents a promising approach for the synthesis of complex molecules. Instead of relying on modifying the functional groups present in a molecule, the synthetic sequence is achieved by carrying out selective reactions on the C-H bonds, which traditionally would have been considered to be the unreactive components of a molecule. A major challenge is to design catalysts to control both the site- and stereoselectivity of the C-H functionalization. We have been developing dirhodium catalysts with different selectivity profiles in C-H functionalization reactions with donor/acceptor carbenes as reactive intermediates. Here we describe a new dirhodium catalyst capable of the functionalization of non-activated primary C-H bonds with high levels of site selectivity and enantioselectivity.

Compressed sensing is a processing method that significantly reduces the number of measurements needed to accurately resolve signals in many fields of science and engineering. We develop a two-dimensional variant of compressed sensing for multidimensional spectroscopy and apply it to experimental data. For the model system of atomic rubidium vapor, we find that compressed sensing provides an order-ofmagnitude (about 10-fold) improvement in spectral resolution along each dimension, as compared to a conventional discrete Fourier transform, using the same data set. More attractive is that compressed sensing allows for random undersampling of the experimental data, down to less than 5% of the experimental data set, with essentially no loss in spectral resolution. We believe that by combining powerful resolution with ease of use, compressed sensing can be a powerful tool for the analysis and interpretation of ultrafast spectroscopy data.

Compressed sensing is a method that allows a significant reduction in the number of samples required for accurate measurements in many applications in experimental sciences and engineering. In this work, we show that compressed sensing can also be used to speed up numerical simulations. We apply compressed sensing to extract information from the real-time simulation of atomic and molecular systems, including electronic and nuclear dynamics. We find that, compared to the standard discrete Fourier transform approach, for the calculation of vibrational and optical spectra the total propagation time, and hence the computational cost, can be reduced by approximately a factor of five.sparse signal reconstruction | molecular dynamics | electron dynamics A recent development in the field of data analysis is the compressed sensing (CS) (or compressive sampling) method (1, 2). The foundation of the method is the concept of sparsity: A signal expanded in a certain basis is said to be sparse when most of the expansion coefficients are zero. This extra information can be used by the CS method to significantly reduce the number of measurements needed to reconstruct a signal. CS has been successfully applied to data acquisition in many different areas (3), including the improvement of the resolution of medical magnetic resonance imaging (4) and the experimental study of atomic and quantum systems (5-7).In this article we show that CS can also be an invaluable tool for some numerical simulations with a considerable reduction of the computational cost. We focus on atomistic simulations of nanoscopic systems by using CS to extract frequency-resolved information from real-time methods such as molecular dynamics (MD) and real-time electron dynamics.MD (8, 9) is one of the most widely used methods to study atomistic systems computationally as it can be used to compute many static and dynamical properties. In MD the trajectory of the atomic nuclei is obtained by integrating their equations of motion either with parametrized force fields or else by explicitly modeling the electrons (10). Given the importance of MD, developing methods that can improve the precision and reduce the computational cost of this method, especially for ab initio MD, can have a large impact in the field of atomistic simulation.Real-time electron dynamics, in particular real-time timedependent density functional theory (TDDFT) (11), plays a similarly important role in the study of linear and nonlinear electronic properties (12-15). Because of its scalability and parallelizability, real-time TDDFT is particularly efficient for large electronic systems (16), so an additional reduction in the computational cost can extend the boundaries of the system sizes that can be studied.Many physical properties are represented by frequency-dependent quantities. To obtain these from real-time information, usually a discrete Fourier transform (FT) is used. Our approach is to replace this FT by a calculation of the Fourier coefficients based on the CS method. To obtain a given ...