Acetyl-CoA carboxylase (ACC) and propionyl-CoA carboxylase (PCC) catalyze the carboxylation of acetyl- and propionyl-CoA to generate malonyl- and methylmalonyl-CoA, respectively. Understanding the substrate specificity of ACC and PCC will (1) help in the development of novel structure-based inhibitors that are potential therapeutics against obesity, cancer, and infectious disease and (2) facilitate bioengineering to provide novel extender units for polyketide biosynthesis. ACC and PCC in Streptomyces coelicolor are multisubunit complexes. The core catalytic beta-subunits, PccB and AccB, are 360 kDa homohexamers, catalyzing the transcarboxylation between biotin and acyl-CoAs. Apo and substrate-bound crystal structures of PccB hexamers were determined to 2.0-2.8 A. The hexamer assembly forms a ring-shaped complex. The hydrophobic, highly conserved biotin-binding pocket was identified for the first time. Biotin and propionyl-CoA bind perpendicular to each other in the active site, where two oxyanion holes were identified. N1 of biotin is proposed to be the active site base. Structure-based mutagenesis at a single residue of PccB and AccB allowed interconversion of the substrate specificity of ACC and PCC. The di-domain, dimeric interaction is crucial for enzyme catalysis, stability, and substrate specificity; these features are also highly conserved among biotin-dependent carboxyltransferases. Our findings enable bioengineering of the acyl-CoA carboxylase (ACCase) substrate specificity to provide novel extender units for the combinatorial biosynthesis of polyketides.

The first committed step of fatty acid and polyketides biosynthesis, the biotin-dependent carboxylation of an acyl-CoA, is catalyzed by acyl-CoA carboxylases (ACCases) such as acetyl-CoA carboxylase (ACC) and propionyl-CoA carboxylase (PCC). ACC and PCC in Streptomyces coelicolor are homologues multisubunit complexes that can carboxylate different short chain acyl-CoAs. While ACC is able to carboxylate acetyl-, propionyl-, or butyryl-CoA with approximately the same specificity, PCC only recognizes propionyl- and butyryl-CoA as substrates. How ACC and PCC have such different specificities towards these substrates is only partially understood. To further understand the molecular basis of how the active site residues can modulate the substrate recognition, we mutated D422, N80, R456 and R457 of PccB, the catalytic beta subunit of PCC. The crystal structures of six PccB mutants and the wild type crystal structure were compared systematically to establish the sequence-structure-function relationship that correlates the observed substrate specificity towards acetyl-, propionyl- and butyryl-CoA with active site geometry. The experimental data confirmed that D422 is a key determinant of substrate specificity, influencing not only the active site properties but further altering protein stability and causing long-range conformational changes. Mutations of N80, R456 and R457 lead to variations in the quaternary structure of the beta subunit and to a concomitant loss of enzyme activity, indicating the importance of these residues in maintaining the active protein conformation as well as a critical role in substrate binding.

Carbonic anhydrases are enzymes capable of transforming carbon dioxide into bicarbonate to maintain functionality of biological systems. Synthetic isolation and implementation of carbonic anhydrases into membrane have recently raised hopes for emerging and efficient strategies that could reduce greenhouse emission and the footprint of anthropogenic activities. However, implementation of such enzymes is currently challenged by the resulting membrane’s wetting capability, overall membrane performance for gas sensing, adsorption and transformation, and by the low solubility of carbon dioxide in water, the required medium for enzyme functionality. We developed the next generation of enzyme-based interfaces capable to efficiently adsorb and reduce carbon dioxide at room temperature. For this, we integrated carbonic anhydrase with a hydrophilic, user-synthesized metal–organic framework; we showed how the framework’s porosity and controlled morphology contribute to viable enzyme binding to create functional surfaces for the adsorption and reduction of carbon dioxide. Our analysis based on electron and atomic microscopy, infrared spectroscopy, and colorimetric assays demonstrated the functionality of such interfaces, while Brunauer–Emmett–Teller analysis and gas chromatography analysis allowed additional evaluation of the efficiency of carbon dioxide adsorption and reduction. Our study is expected to impact the design and development of active interfaces based on enzymes to be used as green approaches for carbon dioxide transformation and mitigation of global anthropogenic activities.

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Manufacturing, processing, use, and disposal of nanoclay-enabled composites potentially lead to the release of nanoclay particles from the polymer matrix in which they are embedded; however, exposures to airborne particles are poorly understood.

Motivation: Precise tracking of individual cells—especially tracking the family lineage, for example in a developing embryo—has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation “model” of any scene that we observe. Because we understand simple truths about the world—for example cells can move and divide, but they cannot instantaneously move vast distances—this model “in our heads” helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. Results: Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby “Universes” and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presense of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.

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