Biodegradation of synthetic polymers, in particular polyethylene terephthalate (PET), is of great importance, since environmental pollution with PET and other plastics has become a severe global problem. Here, we report on the polyester degrading ability of a novel carboxylic ester hydrolase identified in the genome of the marine hydrocarbonoclastic bacterium Pseudomonas aestusnigri VGXO14 T. The enzyme, designated PE-H, belongs to the type IIa family of PET hydrolytic enzymes as indicated by amino acid sequence homology. It was produced in Escherichia coli, purified and its crystal structure was solved at 1.09 Å resolution representing the first structure of a type IIa PET hydrolytic enzyme. The structure shows a typical α/β-hydrolase fold and high structural homology to known polyester hydrolases. PET hydrolysis was detected at 30 • C with amorphous PET film (PETa), but not with PET film from a commercial PET bottle (PETb). A rational mutagenesis study to improve the PET degrading potential of PE-H yielded variant PE-H (Y250S) which showed improved activity, ultimately also allowing the hydrolysis of PETb. The crystal structure of this variant solved at 1.35 Å resolution allowed to rationalize the improvement of enzymatic activity. A PET oligomer binding model was proposed by molecular docking computations. Our results indicate a significant potential of the marine bacterium P. aestusnigri for PET degradation.
BackgroundLight, oxygen, voltage (LOV) domains are widely distributed in plants, algae, fungi, bacteria, and represent the photo-responsive domains of various blue-light photoreceptor proteins. Their photocycle involves the blue-light triggered adduct formation between the C(4a) atom of a non-covalently bound flavin chromophore and the sulfur atom of a conserved cysteine in the LOV sensor domain. LOV proteins show considerable variation in the structure of N- and C-terminal elements which flank the LOV core domain, as well as in the lifetime of the adduct state.ResultsHere, we report the photochemical, structural and functional characterization of DsLOV, a LOV protein from the photoheterotrophic marine α-proteobacterium Dinoroseobacter shibae which exhibits an average adduct state lifetime of 9.6 s at 20°C, and thus represents the fastest reverting bacterial LOV protein reported so far. Mutational analysis in D. shibae revealed a unique role of DsLOV in controlling the induction of photopigment synthesis in the absence of blue-light. The dark state crystal structure of DsLOV determined at 1.5 Å resolution reveals a conserved core domain with an extended N-terminal cap. The dimer interface in the crystal structure forms a unique network of hydrogen bonds involving residues of the N-terminus and the β-scaffold of the core domain. The structure of photoexcited DsLOV suggests increased flexibility in the N-cap region and a significant shift in the Cα backbone of β strands in the N- and C-terminal ends of the LOV core domain.ConclusionsThe results presented here cover the characterization of the unusual short LOV protein DsLOV from Dinoroseobacter shibae including its regulatory function, extremely fast dark recovery and an N-terminus mediated dimer interface. Due to its unique photophysical, structural and regulatory properties, DsLOV might thus serve as an alternative model system for studying light perception by LOV proteins and physiological responses in bacteria.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-015-0365-0) contains supplementary material, which is available to authorized users.
A feasibility study to couple high throughput screening of packed bed chromatography with mass spectrometric detection by SELDI-TOF MS is presented. As model system monoclonal antibodies (mAb) versus host cell protein (HCP) from an industrial cultivation was chosen. Packed bed chromatography was screened on a TECAN Evo Freedom 200 station using miniaturized chromatographic columns placed on a specially designed array carrier linked to a commercially available T-Stack module. Gradient elution of the bound proteins was performed by applying a multiple step strategy. When analyzing selected HCP peaks as well as the detected antibody peaks throughout the chromatographic runs a direct correlation between applied and detected components was established. The sensitivity of conventional protein A chromatography was found to be lower than SELDI-TOF MS analysis. During initial screening a shift in the elution pattern for one of the monoclonal antibodies detected with all four resins was identified to be a heterogeneity in the mAb glycosylation pattern. In addition, a detailed differentiation between various HCP fractions through out the chromatographic process using SELDI-TOF analysis let to the detection of HCP components possibly adhering to the mAbs during chromatographic separations.
Chromatographic processes can be optimized in various ways. However, the two most prominent approaches are either based on statistical data analysis or on experimentally validated simulation models. Both approaches heavily rely on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. Hence, here a closed-loop optimization strategy is proposed in that an automated high throughput liquid handling platform is combined with a genetic algorithm. This setup enables process optimization on the mini-scale and thus saves time as well as material costs. The practicability and robustness of the proposed high throughput method is demonstrated with two exemplary optimization tasks: first, optimization of the buffer composition in the capture step for a binary protein mixture (lysozyme and cytochrome), and second, optimization of multilinear gradient elution for the separation of a ternary mixture (ribonuclease and cytochrome, and lysozyme). IntroductionChromatography is widely used as a separation technique in the biotechnological industry. High selectivity and gentle conditions have made it an essential step in current purification processes for biological macromolecules, for instance, proteins. However, due to complex and dynamic interactions between protein molecules and adsorbent materials, the design of optimal separation processes is very difficult and time consuming. Heuristic design methods that are based on previous experiences with similar separation problems require a great amount of expert knowledge and usually do not lead to the global process optimum. Furthermore, process optimization is often restricted by time-to-market requirements and must, hence, be performed as fast as possible.Most methods for process optimization that are found in the literature today divide into two classes: model-based optimization and direct process optimization. In model-based optimization, mathematical models are utilized to mimic the studied processes. Optimization is performed in-silico and thus has the clear advantage of not restricting the optimization by lab schedules. Limiting factors are only computational effort and reliability or validity of the applied simulation models. The development of mechanistic models requires good process understanding, initial experiments for parameter estimation, and independent experiments for model validation. The latter is also true for black box models (for example [1,2]). The determination of mechanistic model parameters, such as effective mass transfer coefficients and isotherm coefficients, is generally very complex and requires large amounts of material and time, especially when the interactions of realistic multi-component mixtures are considered without significant model simplifications.An alternative to the model-based approach is to directly identify process optima based on the results of experiments that are iteratively planned by an optimization algorithm, such as repeated design of experiments (DoE) or an evolutionary strat...
Chromatographic processes can be optimized in various ways, and the two most prominent approaches are based either on statistical data analysis or on experimentally validated simulation models. Both strategies rely heavily on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. The latter approach is followed in this work, and the utilizability of high throughput compatible experiments for the determination of model parameters which are required for in silico process optimization, is assessed. The unknown parameter values are estimated from batch uptake experiments on a robotic platform and from dynamic breakthrough experiments with miniaturized chromatographic columns. The identified model is then validated with respect to process optimization by comparison of model predictions with experimental data from a preparative scale column. In this study, a strong cation exchanger Toyopearl SP-650M and lysozyme solved in phosphate buffer (pH 7), is used as the test system. The utilization of data from miniaturized and high throughput compatible experiments is shown to yield sufficiently accurate results, and minimizes efforts and costs for both parameter estimation and model validation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.