Gold microplates were synthesized in aqueous solutions by reducing HAuCl4 with the hydroxyl groups in both serine and threonine of bovine serum albumin (BSA), which is a globular protein in its native state. In this article, we systematically investigated the effects of temperature, pH value, the concentration of BSA, and ionic species on the reduction kinetics and thus the size and morphology of the final product. The optimal experimental conditions for producing uniform Au microplates include the following: an elevated temperature in the range of 55–65 °C, an acidic solution with pH ≈ 3, and in the presence of NaCl (0.14 M). We found that if any one of these parameters was deviated from the optimal condition, Au microplates would not be formed in high yields. We also found that the surfaces of the as-synthesized Au microplates were covered by a dense array of BSA bumps.

In the production of chemicals via microbial fermentation, achieving a high yield is one of the most important objectives. We developed a statistical model to analyze influential factors that determine product yield by compiling data obtained from engineered Escherichia coli developed within last 10 years. Using both numerical and ordinal variables (e.g., enzymatic steps, cultivation conditions, and genetic modifications) as input parameters, our model revealed that cultivation modes, nutrient supplementation, and oxygen conditions were the three significant factors for improving product yield. Generally, the model showed that product yield decreases as the number of enzymatic steps in the biosynthesis pathway increases (7-9% loss of yield per enzymatic step). Moreover, overexpression of enzymes or removal of competitive pathways (e.g., knockout) does not necessarily result in an amplification of product yield (P-value>0.1), possibly because of limited capacity in the biosynthesis pathway to accommodate an increase in flux. The model not only provides general guidelines for metabolic engineering and fermentation processes, but also allows a priori estimation and comparison of product yields under designed cultivation conditions.

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. 13C-based metabolic flux analysis (13C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both 13C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

C-isotope labeling is a commonly used technique for determining and quantifying pathways in microorganisms under various growth conditions. The experimental protocol consists of feeding the cell with a composition-defined substrate and measuring isotopic labeling patterns in the synthesized metabolites (often the amino acids). Not only can the labeling information be cross-referenced with genomic information to identify the novel pathways, but it can also be used to decipher absolute carbon fluxes through the metabolic network of interest. This technique can be widely used for functional characterization of nonmodel microbial species, and thus we provide a (13)C-pathway and flux analysis protocol. The five key procedures are: (1) growing cells using labeled substrates, (2) measuring extracellular metabolite and biomass component, (3) analyzing isotopic labeling patterns in amino acids and central metabolites using gas chromatography-mass spectrometry, (4) tracing (13)C carbon transitions in metabolites and discovering new pathways, and (5) estimating flux distributions based on isotopomer constraints. This protocol provides complementary information to the recently published protocol for (13)C-based metabolic flux analysis of the model species Escherichia coli (Nat Protoc 4:878-892, 2009).

We present a molecular-scale model of Bacteriochlorophyll a (BChl a) binding to the chlorosome protein A (CsmA) of Chlorobaculum tepidum, and the aggregated pigment–protein dimer, as determined from protein–ligand docking and quantum chemistry calculations. Our calculations provide strong evidence that the BChl a molecule is coordinated to the His25 residue of CsmA, with the magnesium center of the bacteriochlorin ring situated\3 A° from the imidazole nitrogen atom of the histidine sidechain, and the phytyl tail aligned along the nonpolar residues of the a-helix of CsmA. We also confirm that the Qy band in the absorption spectra of BChl a experiences a large (?16 to ?43 nm) redshift when aggregated with another BChl a molecule in the CsmA dimer, compared to the BChl a in solvent; this redshift has been previously established by experimental researchers. We propose that our model of the BChl a–CsmA binding motif, where the dimer contains parallel aligned N-terminal regions, serves as the smallest repeating unit in a larger model of the para-crystalline chlorosome baseplate protein.