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Modular Total Synthesis in Reticular Chemistry
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Controllable protein phase separation and modular recruitment to form responsive membraneless organelles
Abstract Many intrinsically disordered proteins self-assemble into liquid droplets that function as membraneless organelles. Because of their biological importance and ability to colocalize molecules at high concentrations, these protein compartments represent a compelling target for bio-inspired materials engineering. Here we manipulated the intrinsically disordered, arginine/glycine-rich RGG domain from the P granule protein LAF-1 to generate synthetic membraneless organelles with controllable phase separation and cargo recruitment. First, we demonstrate enzymatically triggered droplet assembly and disassembly, whereby miscibility and RGG domain valency are tuned by protease activity. Second, we control droplet composition by selectively recruiting cargo molecules via protein interaction motifs. We then demonstrate protease-triggered controlled release of cargo. Droplet assembly and cargo recruitment are robust, occurring in cytoplasmic extracts and in living mammalian cells. This versatile system, which generates dynamic membraneless organelles with programmable phase behavior and composition, has important applications for compartmentalizing collections of proteins in engineered cells and protocells.
Modular DNA strand-displacement controllers for directing material expansion
Abstract Soft materials that swell or change shape in response to external stimuli show extensive promise in regenerative medicine, targeted therapeutics, and soft robotics. Generally, a stimulus for shape change must interact directly with the material, limiting the types of stimuli that may be used and necessitating high stimulus concentrations. Here, we show how DNA strand-displacement controllers within hydrogels can mediate size change by interpreting, amplifying, and integrating stimuli and releasing signals that direct the response. These controllers tune the time scale and degree of DNA-crosslinked hydrogel swelling and can actuate dramatic material size change in response to <100 nM of a specific biomolecular input. Controllers can also direct swelling in response to small molecules or perform logic. The integration of these stimuli-responsive materials with biomolecular circuits is a major step towards autonomous soft robotic systems in which sensing and actuation are implemented by biomolecular reaction networks.
Single- and multi-component chiral supraparticles as modular enantioselective catalysts
Nanoscale biological assemblies exemplified by exosomes, endosomes and capsids, play crucial roles in all living systems. Supraparticles (SP) from inorganic nanoparticles (NPs) replicate structural characteristics of these bioassemblies, but it is unknown whether they can mimic their biochemical functions. Here, we show that chiral ZnS NPs self-assemble into 70–100 nm SPs that display sub-nanoscale porosity associated with interstitial spaces between constituent NPs. Similarly to photosynthetic bacterial organelles, these SPs can serve as photocatalysts, enantioselectively converting L- or D-tyrosine (Tyr) into dityrosine (diTyr). Experimental data and molecular dynamic simulations indicate that the chiral bias of the photocatalytic reaction is associated with the chiral environment of interstitial spaces and preferential partitioning of enantiomers into SPs, which can be further enhanced by co-assembling ZnS with Au NPs. Besides replicating a specific function of biological nanoassemblies, these findings establish a path to enantioselective oxidative coupling of phenols for biomedical and other needs.
Modular dimerization of organic radicals for stable and dense flow battery catholyte
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Targeting MYC with modular synthetic transcriptional repressors derived from bHLH DNA-binding domains
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Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds
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Synthesis, modular composition, and electrochemical properties of lamellar iron sulfides
Transition metal chalcogenides with layered structures are promising materials for energy storage, catalysis, and electronics, among other areas. We have identified a new layered phase of iron sulfide containing interlayer solvated cations. We present an optimized synthesis for the Li + -containing material from an Fe(III) xanthate complex. Structure and composition data indicate the material consists of poorly-ordered iron sulfide layers separated by solvated cations. The lamellar spacing in these materials can be tuned by changing the identity of the cation. Furthermore, the lamellar spacing can also be reversibly tuned by the degree of solvation of the material. The material is electrically conductive and can serve as a pseudocapacitor with comparable performance to commercial materials such as MnO 2 . Finally, these materials also show promise as lithium or sodium ion battery cathodes with good capacity and reversibility.
Rxn Rover: automation of chemical reactions with user-friendly, modular software
Automation of chemical reactions through tools such as Rxn Rover in research and development is an enabling technology to reduce cost and waste management in technology transformations towards renewable feedstocks and energy in the chemical industry.
A sequential cyclization/π-extension strategy for modular construction of nanographenes enabled by stannole cycloadditions
Stannoles are introduced as a new, spontaneously aromatizing diene for [4 + 2] cycloadditions that can be easily introduced into diverse conjugated systems, facilitating the efficient synthesis of complex PAHs and their π-extension.
Towards a modular architecture for science factories
Advances in robotic automation, high-performance computing, and artificial intelligence encourage us to propose large, general-purpose science factories with the scale needed to tackle large discovery problems and to support thousands of scientists.
Atomate2: modular workflows for materials science
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
Correction: Atomate2: modular workflows for materials science
[This corrects the article DOI: 10.1039/D5DD00019J.].
Characterization of Ultrahigh-Performance Concrete Materials for Application in Modular Construction
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Modular portable unit for thermal conductivity measurements in multiple cryogenic/magnetic field environments
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