The interaction of lipolytic enzymes with anionic surfactants is of great interest with respect to industrially produced detergents. Here, we report the interaction of cutinase from the thermophilic fungus Humicola insolens with the anionic surfactant SDS, and show the enzyme specifically binds a single SDS molecule under nondenaturing concentrations. Protein interaction with SDS was investigated by NMR, ITC and molecular dynamics simulations. The NMR resonances of the protein were assigned, with large stretches of the protein molecule not showing any detectable resonances. SDS is shown to specifically interact with the loops surrounding the catalytic triad with medium affinity (K a % 10 5 M 21 ). The mode of binding is closely similar to that seen previously for binding of amphiphilic molecules and substrate analogues to cutinases, and hence SDS acts as a substrate mimic. In addition, the structure of the enzyme has been solved by X-ray crystallography in its apo form and after cocrystallization with diethyl p-nitrophenyl phosphate (DNPP) leading to a complex with monoethylphosphate (MEP) esterified to the catalytically active serine. TheAbbreviations: AA, all-atom; AoC, Aspergillus oryzae cutinase; AOT, sodium bis(2-ethylhexyl) sulfosuccinate; cmc, critical micelle concentration; DEP, diethylphosphate; DNPP, diethyl p-nitrophenyl phosphate; EDTA, ethylene diamine tetraacetate; FsC, Fusarium solani cutinase; GcC, Glomerella cingulata cutinase; HiC, Humicola insolens cutinase; HSQC, heteronuclear singlequantum coherence; IPTG, isopropyl b-D-1-thiogalactopyranoside; ITC, isothermal titration calorimetry; MD, molecular dynamics; MEP, monoethylphosphate; MWCO, molecular weight cut-off; PAGE, polyacrylamide gel electrophoresis; RMSD, root mean square deviation; RMSF, root mean square fluctuation; TES, N-[tris(hydroxymethyl)methyl]-2-aminoethanesulfonic acid; SANS, small-angle neutron scattering; SDS, sodium dodecyl sulfate.Additional Supporting Information may be found in the online version of this article.An interactive view is available in the electronic version of the article. enzyme has the same fold as reported for other cutinases but, unexpectedly, esterification of the active site serine is accompanied by the ethylation of the active site histidine which flips out from its usual position in the triad.

The formation of pH gradients in a 700 L batch fermentation of Streptococcus thermophilus was studied using multi-position pH measurements and computational fluid dynamics (CFD) modeling. To this end, a dynamic, kinetic model of S. thermophilus and a pH correlation were integrated into a validated one-phase CFD model, and a dynamic CFD simulation was performed. First, the fluid dynamics of the CFD model were validated with NaOH tracer pulse mixing experiments. Mixing experiments and simulations were performed whereas multiple pH sensors, which were placed vertically at different locations in the bioreactor, captured the response.A mixing time of about 46 s to reach 95% homogeneity was measured and predicted at an impeller speed of 242 rpm. The CFD simulation of the S. thermophilus fermentation captured the experimentally observed pH gradients between a pH of 5.9 and 6.3, which occurred during the exponential growth phase. A pH higher than 7 was predicted in the vicinity of the base solution inlet. Biomass growth, lactic acid production, and substrate consumption matched the experimental observations. Moreover, the biokinetic results obtained from the CFD simulation were similar to a single-compartment simulation, for which a homogeneous distribution of the pH was assumed. This indicates no influence of pH gradients on growth in the studied bioreactor. This study verified that the pH gradients during a fermentation in the pilot-scale bioreactor could be accurately predicted using a coupled simulation of a biokinetic and a CFD model. To support the understanding and optimization of industrial-scale processes, future biokinetic CFD studies need to assess multiple types of environmental gradients, like pH, substrate, and dissolved oxygen, especially at industrial scale. K E Y W O R D S computational fluid dynamics (CFD), dynamic simulation, heterogeneities, lactic acid bacteria (LAB) fermentation, pH gradients, transient CFD simulation 1 | INTRODUCTION Heterogeneities of culture parameters like substrate concentrations, pH, and dissolved oxygen concentrations are regarded as mainly responsible for productivity loss in large-scale bioreactor cultivations.Transport limitations occur at large scale due to insufficient mixing, and cells are consequently exposed to fluctuating conditions. Non-limiting substrate concentrations in the range of 0.3-2 g L −1 are reported in feeding zones during fed-batch processes, whereas there are substrate-limited conditions further away from the feeding Biotechnology and Bioengineering. 2019;116:769-780.wileyonlinelibrary.com/journal/bit

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

No abstract

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.