An effective family shuffling method using single-stranded DNA

Family shuffling, which is one of the most powerful techniques for in vitro protein evolution, always involves the problem of reassembling the gene fragments into parental gene sequences, because such a process prevents the formation of chimeric sequences. In order to improve the efficiency of hybrid formation in family shuffling, single-stranded DNAs (ssDNAs) were used as templates. The ssDNAs of two catechol 2,3-dioxygenase genes, nahH and xylE, were prepared, the xylE strand being complementary to the nahH strand. When these ssDNAs were digested by DNase I and reassembled, chimeric genes were obtained at a rate of 14%, which was much higher than the rate of less than 1% obtained by shuffling with double-stranded DNAs. Chimeric catechol 2,3-dioxygenases that were more thermally stable than the parental enzymes, XylE and NahH, were obtained by this ssDNA-based DNA shuffling.     

A novel method for single-grain-based metabolic profiling of Arabidopsis seed

Introduction

In plant metabolomics, metabolite contents are often normalized by sample weight. However, accurate weighing of very small samples, such as individual Arabidopsis thaliana seeds (approximately 20 µg), is difficult, which may lead to irreproducible results.

Objectives

We aimed to establish alternative normalization methods for seed-grain-based comparative metabolomics of A. thaliana.

Methods

Arabidopsis thaliana seeds were assumed to have a prolate spheroid shape. Using a microscope image of each seed, the lengths of major and minor axes were measured by fitting a projected 2-dimensional shape of each seed as an ellipse. Metabolic profiles of individual diploid or tetraploid A. thaliana seeds were measured by our highly sensitive protocol (“widely targeted metabolomics”) that uses liquid chromatography coupled with tandem quadrupole mass spectrometry. Mass spectrometric analysis of 1 µL of solution extract identified more than 100 metabolites. The data were normalized by various seed-size measures, including seed volume (single-grain-based analysis). For comparison, metabolites were extracted from 4 mg of diploid and tetraploid A. thaliana seeds and their metabolic profiles were analyzed by normalization of weight (weight-based analysis).

Results

A small number of metabolites showed statistically significant differences in the single-grain-based analysis compared to weight-based analysis. A total of 17 metabolites showed statistically different accumulation between ploidy types with similar fold changes in both analyses.

Conclusion

Seed-size measures obtained by microscopic imaging were useful for data normalization. Single-grain-based analysis enables evaluation of metabolism of each seed and elucidates the metabolic profiles of precious bioresources by using small amounts of samples.

     

An SVM method using evolutionary information for the identification of allergenic proteins

This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold cross-validation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems.     

An immunoassay method for quantitative detection of proteins using single antibodies

A new immunoassay method called specific analyte labeling and recapture assay (SALRA) to quantitatively measure protein abundance was developed, and the assay conditions were optimized. The key features of this method include labeling the antigen bound to the capture antibody, eluting the labeled antigen, and recapturing it by the same capture antibody on the detection plate. The reporter molecules on the labeled antigen provide a convenient and reliable means for signal detection. We demonstrated that the dose-response curve of SALRA was comparable to that of sandwich enzyme-linked immunosorbent assay (ELISA) and better than that of the antigen direct labeling method. In addition, multiple proteins can be measured simultaneously by SALRA. Using the SALRA method, the detection limit for most of the cytokines tested was approximately 0.01 ng/ml. Further SALRA tests on interleukin 6 (IL-6) showed the linear dose-response was 3.3 to 0.01 ng/ml, the accuracy of the test was 71 to 91%, the intraassay variation was 3.6 to 7.4%, and the interassay variation was 3.8 to 10.0%. The applications of SALRA include quantitatively measuring proteins for which there are no ELISA tools available and providing a new platform for protein microarrays.     

Micro method for determination of reactive carbonyl groups in proteins and peptides, using 2,4-dinitrophenylhydrazine

A method is described for determining carbonyl groups that is especially suitable for use with proteins and peptides. It involves the determination of the extinction at 370nm of a sample solution after adding 2,4-dinitrophenylhydrazine. The reaction of 2,4-dinitrophenylhydrazine with pyruvoylglycine and with transaminated ribonuclease T(1) is presented; the isolation of protein hydrazones is discussed.     

Pre-analytical method for metabolic profiling of plant cell cultures of Passiflora garckei

Passiflora garckei cell cultures were used as a model to describe a reproducible sample preparation method. Solid phase extraction (SPE) was employed to isolate the plant metabolites for nuclear magnetic resonance (NMR) analysis and to subsequently detect the differences between yeast extract elicited and control cells. Compared with previous results obtained by using a Sephadex LH-20 column, SPE coupled with NMR spectroscopy improves the analysis of aromatic compounds e.g.: trans-feruloyl derivatives and trans-coumaroyl derivatives. Moreover, it decreases the concentration of sugars that usually overlap with many plant metabolite signals.     

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

The role of urinary metabolic profiling in systems biology research is expanding. This is because of the use of this technology for clinical diagnostic and mechanistic studies and for the development of new personalized health care and molecular epidemiology (population) studies. The methodologies commonly used for metabolic profiling are NMR spectroscopy, liquid chromatography mass spectrometry (LC/MS) and gas chromatography-mass spectrometry (GC/MS). In this protocol, we describe urine collection and storage, GC/MS and data preprocessing methods, chemometric data analysis and urinary marker metabolite identification. Results obtained using GC/MS are complementary to NMR and LC/MS. Sample preparation for GC/MS analysis involves the depletion of urea via treatment with urease, protein precipitation with methanol, and trimethylsilyl derivatization. The protocol described here facilitates the metabolic profiling of ～400-600 metabolites in 120 urine samples per week.     

An electrophoretic profiling method for thiol-rich phytochelatins and metallothioneins

Thiol-rich peptides such as phytochelatins (PCs) and metallothioneins (MTs) are important cellular chelating agents which function in metal detoxification and/or homeostasis. The variations in molecular sizes and lack of chromophores of these peptides make their analysis difficult. This paper reports an electrophoresis-based method for a broad screen of thiol-rich peptides and proteins. The method uses the thiol-selective fluorescent tag, monobromobimane, coupled with Tricine--sodium dodecyl sulphate--urea polyacrylamide gel electrophoresis for a sensitive determination of both PCs and MTs. Results for PCs were confirmed by two-dimensional NMR and HPLC-tandem MS analyses. Sample throughput is substantially improved over chromatography-based methods through parallel sample analysis in 1 h of electrophoretic separation. The method is versatile in that peptides ranging from glutathione to large proteins can be analysed by simple modification(s) of the extraction and electrophoretic conditions, and the nature of the method supports serendipitous detection of unexpected or novel thiol metabolites.     

An effective method to increase solvability in biochemical systems using S-system

In this paper we propose an effective method to estimate the intrinsic values in an immobilized enzyme system, i.e., Michaelis constant K(m) and the maximum reaction rate V(m). We combine three techniques: (1) the non-linear least square method for estimating the kinetic values, (2) orthogonal collocation with the Gauss integration method, and (3) Newton-Raphson method (NRM) or S-system method (SSM) as Newton-like method. We build a procedure to combine the first two methods to estimate the unknown kinetic values in a system. We apply this procedure to solve the intrinsic kinetic parameters determination problem in an immobilized enzyme systems following Michaelis-Menten reaction. To demonstrate the effectiveness of the current method, we test their convergence performance in detail. The results show that the basin of attraction in the current method is extremely enlarged compared with that of the S-system alone. We suggest that the current method is one of the most effective ways to solve fairly complicated biochemical reaction systems in general.     

iTRAQ-based profiling of grape berry exocarp proteins during ripening using a parallel mass spectrometric method

The 4-plex iTRAQ platform was utilized to analyze the protein profiles in four stages of grapevine berry skin ripening, from pre-veraison to fully ripening. Mass spectrometric data were acquired from three replicated analyses using a parallel acquisition method in an Orbitrap instrument by combining collision-induced dissociation (CID) and higher energy collision-induced dissociation (HCD) peptide ion fragmentations. As a result, the number of spectra suitable for peptide identification (either from CID or HCD) increased 5-fold in relation to those suitable for quantification (from HCD). Spectra were searched against an NCBInr protein database subset containing all the Vitis sequences, including those derived from whole genome sequencing. In general, 695 unique proteins were identified with more than one single peptide, and 513 of them were quantified. The sequence annotation and GO term enrichment analysis assisted by the automatic annotation tool Blast2GO permitted a pathway analysis which resulted in finding that biological processes and metabolic pathways de-regulated throughout ripening. A detailed analysis of the function-related proteins profiles helped discover a set of proteins of known Vitis gene origin as the potential candidates to play key roles in grapevine berry quality, growth regulation and disease resistance.     

An automated method for modeling proteins on known templates using distance geometry.

We present an automated method incorporated into a software package, FOLDER, to fold a protein sequence on a given three-dimensional (3D) template. Starting with the sequence alignment of a family of homologous proteins, tertiary structures are modeled using the known 3D structure of one member of the family as a template. Homologous interatomic distances from the template are used as constraints. For nonhomologous regions in the model protein, the lower and the upper bounds for the interatomic distances are imposed by steric constraints and the globular dimensions of the template, respectively. Distance geometry is used to embed an ensemble of structures consistent with these distance bounds. Structures are selected from this ensemble based on minimal distance error criteria, after a penalty function optimization step. These structures are then refined using energy optimization methods. The method is tested by simulating the alpha-chain of horse hemoglobin using the alpha-chain of human hemoglobin as the template and by comparing the generated models with the crystal structure of the alpha-chain of horse hemoglobin. We also test the packing efficiency of this method by reconstructing the atomic positions of the interior side chains beyond C beta atoms of a protein domain from a known 3D structure. In both test cases, models retain the template constraints and any additionally imposed constraints while the packing of the interior residues is optimized with no short contacts or bond deformations. To demonstrate the use of this method in simulating structures of proteins with nonhomologous disulfides, we construct a model of murine interleukin (IL)-4 using the NMR structure of human IL-4 as the template. The resulting geometry of the nonhomologous disulfide in the model structure for murine IL-4 is consistent with standard disulfide geometry.     

Proteome profiling and its use in metabolic and cellular engineering

Proteome profiling of microorganisms makes it possible to generate valuable knowledge that can be used for the development of metabolic and cellular engineering strategies, which consequently are used to enhance the yield and productivity of native or foreign bioproducts and to modify cellular properties to improve mid-stream and down-stream processes. Advances in two-dimensional gel electrophoresis technology combined with mass spectrometry allow the creation of global scale proteome contents which can be used to elucidate valuable information on the dynamics of the metabolic, signaling and regulatory networks apart from understanding the physiological changes. In this paper, we review the approaches of exploiting the proteome profiling results to the development of the strategies for the metabolic and cellular engineering of microorganisms.     

An automated method for scanning LC-MS data sets for significant peptides and proteins, including quantitative profiling and interactive confirmation

Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.     

An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles

Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications of bioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in a genome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. These methods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identify the influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Here we propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derive the basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to target genomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogenetic relationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in the current analysis. We compared our approach with two existing methods and our initial results show that the predictions have outperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-protein interaction network derived from protein-protein interaction database STRING. Our preliminary results indicates that improvement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the same scale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotating hypothetical proteins from prokaryotic genomes.