Multi‐spectroscopic and molecular dynamics simulations investigation of the binding mechanism of polybrominated diphenyl ethers to hen egg white lysozyme

Three PBDEs (BDE25, BDE47, and BDE154) were selected to investigate the interactions between PBDEs and hen egg white lysozyme (HEWL) by molecular modeling, fluorescence spectroscopy, and FT‐IR spectra. The docking results showed that hydrogen bonds were formed between BDE25 and residue TRP63 and between BDE47 and TRP63 with bond lengths of 2.178 Å and 2.146 Å, respectively. The molecular dynamics simulations indicated that van der Waals forces played a predominant role in the binding of three PBDEs to HEWL. The observed fluorescence quenching can be attributed to the formation of complexes between HEWL and PBDEs, and the quenching mechanism is a static quenching. According to Förster's non‐radiative energy transfer theory, the binding distances r were < 7 nm, indicating a high probability of energy transfer from HEWL to the three PBDEs. The synchronous fluorescence showed that the emission maximum wavelength of tryptophan (TRP) residues emerged a red‐shift. FT‐IR spectra indicated that BDE25, BDE47 and BDE154 induced the α‐helix percentage of HEWL decreased from 32.70% ± 1.64% to 28.27% ± 1.41%, 27.50% ± 1.38% and 29.78% ± 1.49%, respectively, whereas the percentage of random coil increased from 26.67% ± 1.33% to 27.60% ± 1.38%, 29.18% ± 1.46% and 30.59% ± 1.53%, respectively.     

DECA,A Comprehensive,Automatic Post-processing Program for HDX-MS Data*

1. Download : Download high-res image (128KB)
2. Download : Download full-size image

Highlights

• •Open source software for comprehensive HDX-MS data analysis.
• •Automatic back-exchange correction options.
• •Rigorous statistical analysis of the significance of uptake differences.
• •High quality visualization tools.

     

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins*

1. Download : Download high-res image (51KB)
2. Download : Download full-size image

Highlights

• •Method for the analysis of response curves from thermal proteome profiling (TPP).
• •NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control.
• •Increased proteome coverage and sensitivity to identify drug-binding proteins.

     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

A Predictive Mathematical Model of Cell Cycle,Metabolism, and Apoptosis of Monoclonal Antibody‐Producing GS–NS0 Cells

The monoclonal antibody (mAb) industry is witnessing unprecedented growth, with an increasing range of new molecules and biosimilars as well as disease targets approved than ever before. Competition necessitates pharmaceutical companies to reduce development/production costs and time‐to‐market. To this aim, mathematical modeling can aid traditional experiment‐only‐based process development by reducing the design space, integrating scales, and assisting in identifying optimal operating conditions in less time and with lower expense. Mathematical models have been employed by other industries for control and optimization purposes and are important decisional tools for testing scenarios, process configurations, operating conditions, etc. Herein, a predictive, experimentally validated mathematical model that captures cellular metabolism and growth with cell cycle, cell death (apoptosis), and mAb production in GS–NS0 cells is presented. The model utilizes cellular, metabolic, and gene expression data, highlighting how multiple data sources can be integrated in one tool with the aim of optimizing mammalian cell bioprocessing.     

In Silico Metabolic Design of Two‐Strain Biofilm Systems Predicts Enhanced Biomass Production and Biochemical Synthesis

Engineered biofilm consortia have the potential to solve important biotechnological problems that have proved difficult for monoculture biofilms and planktonic consortia, such as conversion of lignocellulosic material to useful biochemicals. While considerable experimental progress has been reported for engineering and characterizing biofilm consortia, the field still lacks in silico tools for simulation, design, and optimization of stable, robust, and productive designed consortia. We developed biofilm consortia metabolic models for two coculture systems centered around the ecological design motif of a primary cell type that utilizes a supplied electron donor and secretes acetate as a byproduct and a secondary cell type that consumes the acetate, relieving byproduct inhibition on the primary cell type and enhancing overall system biomass. The models presented in this paper predict that distinct metabolic niches for the two cell types could be established by supplying electron donors and acceptors at opposite ends of the biofilm and that acetate consumption by the secondary cell type could increase total biomass accumulation and the synthesis of valuable biochemicals, such as isobutanol, by the primary cell type. System tunability is enhanced when each cell type is supplied with a unique terminal electron acceptor at opposite ends of the biofilm rather than competing for a common electron acceptor. Our model provides good qualitative agreement with data for a synthetic Escherichia coli coculture system, suggesting that the proposed design rules may have wide applicability to engineered biofilm consortia.     

Dynamic imaging and quantification of subcellular motion with eigen‐decomposition optical coherence tomography‐based variance analysis

The dynamic properties of subcellular organism are important biomarkers of the health. Imaging subcellular level dynamics provides effective solutions for evaluating cell metabolism and testing the responses of cells to pathogens and drugs in pharmaceutical engineering. In this paper, we demonstrate an innovative approach to contrast the subcellular motion by using eigen decomposition (ED)‐based variance analysis of time‐dependent complex optical coherence tomography signals. This method reveals a superior advantage of contrast to noise ratio when compared with the approach that employs intensity decorrelation. Furthermore, the eigen values derived from ED processing are calculated and applied to assess the power ratios of complex signal invariance that decreases exponentially along time dimension. The validation experiments are performed on the patterned samples of yeast powder mixed with gelatin/TiO2 water solution. Additionally, the proposed method is used to image mouse cerebral cortex in normal and pathological conditions, suggesting the practicality of variance power mapping in analyzing cortical neural activities. The technique promises efficient measurement of subcellular motions with high sensitivity and high throughput for in vivo and in situ applications.     

Competition for Mitogens Regulates Spermatogenic Stem Cell Homeostasis in an Open Niche

1. Download high-res image (317KB)
2. Download full-size image