ACS Central Science最新文献

A recyclable solid-state electrolyte enabled by a novel aluminum fluoride framework enhances aluminum-ion battery longevity, safety, and cost-efficiency.

C-H functionalization of complex substrates is highly enabling in total synthesis and in the development of late-stage drug candidates. Much work has been dedicated to developing new methods as well as predictive modeling to accelerate route scouting. However, workflows to identify regioisomeric products are arduous, typically requiring chromatographic separation and/or nuclear magnetic resonance spectroscopy analysis. In addition, most reports focus on major products or do not assign regioisomeric products, which biases predictive models constructed from such data. Herein, we present a novel approach to complex reaction analysis utilizing partial deuterium labels, which enables direct product identification via liquid chromatography-mass spectrometry. When combined with spectral deconvolution, the method generates product ratios while circumventing chromatography altogether. Competitive kinetic isotope effects can also be determined. The resultant data are expected to be useful in the construction of predictive models across several dimensions including reaction selectivity, the impact of structure on mechanism, and mass spectral ionization patterns and expedite the identification of drug metabolites.

C–H functionalization of complex substrates is highly enabling in total synthesis and in the development of late-stage drug candidates. Much work has been dedicated to developing new methods as well as predictive modeling to accelerate route scouting. However, workflows to identify regioisomeric products are arduous, typically requiring chromatographic separation and/or nuclear magnetic resonance spectroscopy analysis. In addition, most reports focus on major products or do not assign regioisomeric products, which biases predictive models constructed from such data. Herein, we present a novel approach to complex reaction analysis utilizing partial deuterium labels, which enables direct product identification via liquid chromatography–mass spectrometry. When combined with spectral deconvolution, the method generates product ratios while circumventing chromatography altogether. Competitive kinetic isotope effects can also be determined. The resultant data are expected to be useful in the construction of predictive models across several dimensions including reaction selectivity, the impact of structure on mechanism, and mass spectral ionization patterns and expedite the identification of drug metabolites.

Partial isotopic labels allow direct identification of regioisomers via their distinct isotopic distributions. Alternately, spectral deconvolution of unseparated mixtures delivers regioisomer ratios.

SWIR dyes─The Eras Tour. This love story between chemistry and biology shines.

Synthetic mycobactin−fluorophore conjugates exploit the mycobacterial iron acquisition pathway to enable sensitive fluorogenic detection of Mycobacterium tuberculosis.

Biomolecular condensates composed of highly charged biomolecules, such as DNA, RNA, chromatin, and nucleic-acid binding proteins, are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for multiple systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present Mpipi-Recharged, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multidomain proteins, and/or disordered single-stranded RNAs. Mpipi-Recharged introduces a pair-specific asymmetric Yukawa electrostatic potential, informed by atomistic simulations. We show that this asymmetric coarse-graining of electrostatic forces captures intricate effects, such as charge blockiness, stoichiometry variations in complex coacervates, and modulation of salt concentration, without requiring explicit solvation. Mpipi-Recharged provides excellent agreement with experiments in predicting the phase behavior of highly charged condensates. Overall, Mpipi-Recharged improves the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates, enhancing the scope of computer simulations in this field.

Mpipi-Recharged is a residue-resolution coarse-grained model that innovatively treats screened electrostatic interactions, improving predictions for charged biomolecular condensates and ensuring computational efficiency.

Biomolecular condensates composed of highly charged biomolecules, such as DNA, RNA, chromatin, and nucleic-acid binding proteins, are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for multiple systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present Mpipi-Recharged, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multidomain proteins, and/or disordered single-stranded RNAs. Mpipi-Recharged introduces a pair-specific asymmetric Yukawa electrostatic potential, informed by atomistic simulations. We show that this asymmetric coarse-graining of electrostatic forces captures intricate effects, such as charge blockiness, stoichiometry variations in complex coacervates, and modulation of salt concentration, without requiring explicit solvation. Mpipi-Recharged provides excellent agreement with experiments in predicting the phase behavior of highly charged condensates. Overall, Mpipi-Recharged improves the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates, enhancing the scope of computer simulations in this field.