Protein analysis on a genomic scale

The byssal attachment of California mussels Mytilus californianus provides secure adhesion in the presence of moisture, a feat that still eludes most synthetic polymers. Matrix-assisted laser desorption ionization mass spectrometry was used to probe the footprints of byssal attachment plaques on glass cover slips for adhesive proteins. Besides the abundant mcfp-3 protein family (Zhao, H., Robertson, N. B., Jewhurst, S. A., and Waite, J. H. (2006) J. Biol. Chem. 281, 11090-11096), two new proteins, mcfp-5 and mcfp-6, with masses of 8.9 kDa and 11.6 kDa, respectively, were identified in footprints, partially characterized and completely sequenced from a cDNA library. mcfp-5 resembles mcfp-3 in its basic pI and abundant 3,4-dihydroxyphenyl-L-alanine (Dopa; 30 mol %), but is distinct in two respects: it is more homogeneous in primary sequence and is polyphosphorylated. mcfp-6 is basic and contains a small amount of Dopa (<5 mol %). In contrast to mcfp-3 and -5, tyrosine prevails at 20 mol %, and cysteine is present at 11 mol %, one-third of which remains thiolate. Given the oxidative instability of Dopa and cysteine at pH 8.2 (seawater), we tested the hypothesis that thiols serve to scavenge dopaquinones by adduct formation. Plaque footprints were hydrolyzed and screened for cysteine dopaquinone adducts using phenylboronate affinity chromatography. 5-S-Cysteinyldopa was detected at nearly 1 mol %. The results suggest that mcfp-6 may provide a cohesive link between the surface-coupling Dopa-rich proteins and the bulk of the plaque proteins.

Analysis of amino acid composition in proteins of animal tissues and foods as pre-column o-phthaldialdehyde derivatives by HPLC with fluorescence detection.

The dramatically increasing number of new protein sequences arising from genomics and proteomics requires the need for methods to rapidly and reliably infer the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds in nature, thereby providing three-dimensional folding patterns for all proteins and to infer molecular functions of the proteins based on the combined information of structures and sequences. The goal of obtaining protein structures on a genomic scale has motivated the development of high throughput technologies and protocols for macromolecular structure determination that have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional inferences and evolutionary relationships that were hidden at the sequence level. Here, we present samples of structures determined at Berkeley Structural Genomics Center and collaborators' laboratories to illustrate how structural information provides and complements sequence information to deduce the functional inferences of proteins with unknown molecular functions. Two of the major premises of structural genomics are to discover a complete repertoire of protein folds in nature and to find molecular functions of the proteins whose functions are not predicted from sequence comparison alone. To achieve these objectives on a genomic scale, new methods, protocols, and technologies need to be developed by multi-institutional collaborations worldwide. As part of this effort, the Protein Structure Initiative has been launched in the United States (PSI; www.nigms.nih.gov/funding/psi.html). Although infrastructure building and technology development are still the main focus of structural genomics programs, a considerable number of protein structures have already been produced, some of them coming directly out of semiautomated structure determination pipelines. The Berkeley Structural Genomics Center (BSGC) has focused on the proteins of Mycoplasma or their homologues from other organisms as its structural genomics targets because of the minimal genome size of the Mycoplasmas as well as their relevance to human and animal pathogenicity (http://www.strgen.org). Here we present several protein examples encompassing a spectrum of functional inferences obtainable from their three-dimensional structures in five situations, where the inferences are new and testable, and are not predictable from protein sequence information alone.

Characterization of cuticular proteins from the migratory locust, Locusta migratoria

PurposeThe purpose of this study was to present the determination of inter- and intra-day variations in tear flow rate, and tear fluid protein concentration, as well as protein composition regarding their impact for future biomarker studies.MethodsTear fluid was collected noninvasively from 18 healthy subjects by performing Schirmer tests at 4 different time points repetitive in a period of 2 days. The tear flow rate on the Schirmer test strips was measured. Proteins were extracted from strips and quantified using amino acid analysis. Protein composition was analyzed by the strips data-independent (DIA) based mass spectrometry. To exclude any impairments to health, volunteers underwent a detailed neurological as well as an ophthalmological examination.ResultsWhether tear fluid was collected from oculus sinister or oculus dexter did not affect the tear flow rate (P ≈ 0.63) or protein concentration (P ≈ 0.97) of individual subjects. Moreover, protein concentration was independent from the tear volume, so that a change in volume may only influence the total protein amount. When the examination days were compared, investigation of tear flow rate (P ≈ 0.001) and protein concentration (P ≈ 0.0003) indicated significant differences. Further, mass spectrometric analysis of tear fluid revealed 11 differentially regulated proteins when comparing both examination days.ConclusionsOur findings provide evidence of inter-day variation in tear flow rate, tear proteome concentration, and composition in healthy subjects, suggesting that inter-day variation needs to be taken into consideration in biomarker research of tear fluid. Identified proteins were assigned to functions in the immune response, oxidative and reducing processes, as well as mannose metabolism.

Background: Benveniste’s biology experiments suggested the existence of molecular-like effects without molecules (“memory of water”). In this article, it is proposed that these disputed experiments could have been the consequence of a previously unnoticed and non-conventional experimenter effect. Methods: A probabilistic modelling is built in order to describe an elementary laboratory experiment. A biological system is modelled with two possible states (“resting” and “activated”) and exposed to two experimental conditions labelled “control” and “test”, but both are biologically inactive. The modelling takes into account not only the biological system, but also the experimenters. In addition, an outsider standpoint is adopted to describe the experimental situation. Results: A classical approach suggests that, after experiment completion, the “control” and “test” labels of biologically-inactive conditions should both be associated with the “resting” state (i.e., no significant relationship between labels and system states). However, if the fluctuations of the biological system are also considered, a quantum-like relationship emerges and connects labels and system states (analogous to a biological “effect” without molecules). Conclusions: No hypotheses about water properties or other exotic explanations are needed to describe Benveniste’s experiments, including their unusual features. This modelling could be extended to other experimental situations in biology, medicine, and psychology.

Amniotic fluid is a dynamic and complex mixture that reflects the physiological status of the developing fetus. In this study, the human amniotic fluid (AF) proteome of a 16-18-week normal pregnancy was profiled and analyzed to investigate the composition and functions of this fluid. Due to the complexity of AF, we utilized three different fractionation strategies to provide greater coverage. Two types of two-dimensional LC/MS/MS as well as an LC-SDS-PAGE-LC-MS/MS platform were used. A total of 16 AF samples between gestational ages of 16 and 18 weeks from women carrying chromosomally normal fetuses were analyzed by one of the three fractionation methods followed by a common reverse phase LC-MS/MS step. Mascot and The Global Proteome Machine engines were used to search the International Protein Index human database for peptide sequence identification. The list of proteins was generated by combining the results of both engines through the PeptideProphet of Scaffold software. All identified proteins were combined to generate the AF proteome comprising 1,026 unique gene matches or 842 non-redundant proteins. This list includes most of the currently used biomarkers for pregnancy-associated pathologic conditions such as preterm delivery, intra-amniotic infection, and chromosomal anomalies of the fetus. The subcellular localization, tissue expression, functions, and networks of the AF proteome were analyzed by various bioinformatic tools. These data will contribute to the better understanding of amniotic fluid function and to the discovery of novel biomarkers for prenatal diagnosis of fetal abnormalities.

Proteomics is potentially a powerful technology for elucidating brain function and neurodegenerative diseases. So far, the brain proteome has generally been analyzed by two-dimensional gel electrophoresis, which usually leads to the complete absence of membrane proteins. We describe a proteomic approach for profiling of plasma membrane proteins from mouse brain. The procedure consists of a novel method for extraction and fractionation of membranes, on-membrane digestion, diagonal separation of peptides, and high-sensitivity analysis by advanced MS. Breaking with the classical plasma membrane fractionation approach, membranes are isolated without cell compartment isolation, by stepwise depletion of nonmembrane molecules from entire tissue homogenate by high-salt, carbonate, and urea washes followed by treatment of the membranes with sublytic concentrations of digitonin. Plasma membrane is further enriched by of density gradient fractionation and protein digested on-membrane by endoproteinase Lys-C. Released peptides are separated, fractions digested by trypsin, and analyzed by LC-MS/MS. In single experiments, the developed technology enabled identification of 862 proteins from 150 mg of mouse brain cortex. Further development and miniaturization allowed analysis of 15 mg of hippocampus, revealing 1,685 proteins. More that 60% of the identified proteins are membrane proteins, including several classes of ion channels and neurotransmitter receptors. Our work now allows in-depth study of brain membrane proteomes, such as of mouse models of neurological disease.

Two-dimensional electrophoretic separation and immobilization of proteins onto inert membranes for subsequent amino acid sequence and amino acid composition analysis is described as a rapid procedure for the identification or characterization of proteins from complex mixtures. This method avoids the drawbacks of classical purification and isolation methods which involve time-consuming operations with low resolution and, often, insufficient yields. Excellent overall yields of minor amounts (in the low microgram range) using this method allow for sequence determination of yet inaccessible proteins. Solubilized cell proteins of mouse brain were separated by high resolution two-dimensional electrophoresis and electroblotted onto a siliconized glass fiber membrane. The immobilized proteins were stained with Coomassie Brilliant Blue R-250, and twelve proteins spots were then submitted to both Edman degradation and amino acid analysis. Proteins were identified by comparison of the experimentally determined amino acid composition with a dataset derived from the Protein Identification Resource (PIR) protein sequence database. Eight out of twelve proteins tested were identified by amino acid analysis and confirmed by N-terminal sequence determination.

Transcription factors (TFs) play an important role in gene regulation. Computational identification and annotation of TFs at genome scale are the first step toward understanding the mechanism of gene expression and regulation. We started to construct the database of Arabidopsis TFs in 2005 and developed a pipeline for systematic identification of plant TFs from genomic and transcript sequences. In the following years, we built a database of plant TFs (PlantTFDB, http://planttfdb.cbi.pku.edu.cn ) which contains putative TFs identified from 22 species including five model organisms and 17 economically important plants with available EST sequences. To provide comprehensive information for the putative TFs, we made extensive annotation at both the family and gene levels. A brief introduction and key references were presented for each family. Functional domain information and cross-references to various well-known public databases were available for each identified TF. In addition, we predicted putative orthologs of the TFs in other species. PlantTFDB has a simple interface to allow users to make text queries, or BLAST searches, and to download TF sequences for local analysis. We hope that PlantTFDB could provide the user community with a useful resource for studying the function and evolution of transcription factors.

The pharmacological actions of morphine and morphine-like drugs such as heroin are mediated primarily through the μ opioid receptor. Previously a single strand DNA element of the mouse μ opioid receptor gene (Oprm1) proximal promoter was found to be important for regulating Oprm1 in neuronal cells. To identify proteins binding to the single strand DNA element as potential regulators for Oprm1, affinity column chromatography with the single strand DNA element was performed using neuroblastoma NS20Y cells followed by two-dimensional gel electrophoresis and MALDI-TOF mass spectrometry. We identified five poly(C)-binding proteins: heterogeneous nuclear ribonucleoprotein (hnRNP) K, α-complex proteins (αCP) αCP1, αCP2, αCP2-KL, and αCP3. Binding of these proteins to the single strand DNA element of Oprm1 was sequence-specific as confirmed by supershift assays. In cotransfection studies, hnRNP K, αCP1, αCP2, and αCP2-KL activated the Oprm1 promoter activity, whereas αCP3 acted as a repressor. Ectopic expression of hnRNP K, αCP1, αCP2, and αCP2-KL also led to activation of the endogenous Oprm1 transcripts, and αCP3 repressed endogenous Oprm1 transcripts. We demonstrate novel roles as transcriptional regulators in Oprm1 regulation for hnRNP K and αCP binding to the single strand DNA element.

Microorganisms play an integral and unique role in ecosystem function and sustainability. Understanding the structure and composition of microbial communities and their responses and adaptations to environmental perturbations such as toxic contaminants, climate changes, and agricultural and industrial practices is critical in maintaining or restoring desirable ecosystem functions. The DNA microarray (or microchip) technology is a powerful tool for studying gene expression and regulation on a genomic scale and detecting genetic polymorphism in both prokaryotes and eukaryotes. Compared to conventional membrane-based hybridization, glass slide-based microarrays offer the additional advantages of rapid detection, lower cost, automation, and low background levels. Microarray-based genomic technique is potentially an extremely powerful tool for characterizing microbial communities and their biological functions. Hence, in this chapter, we will focus on the detection of expression level of certain functional genes in Pseudomonas spp., Staphylococcus spp., and viruses.KeywordsMethicillin Resistant Staphylococcus AureusFluidic StationProbe ArraySpecific VirusOrderly ArrangementThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Introduction In the previous chapters, we had discussed methods for studying the genetic content of bacteria on a genomic scale. In the process, we highlighted characteristics of certain bacterial genomes, besides discussing properties common to many bacterial genomes. We also documented intra-specific variation in gene content between bacteria. Different strains of the same bacterial species might show considerable variation in gene content, as a result of mutations getting fixed in a particular context, or the dynamics of gene acquisition and loss. Many of these variations might arise from selection resulting from their niches and/or lifestyles. However, this does not reflect the fact that the same bacterium might be phenotypically distinct under different environmental or cellular contexts. Much of these differences might be attributed to gene expression changes, i.e., under a given condition, a bacterium expresses only a subset of its genes, with the remaining genes being silent. Or, the expression level of the same gene might be quantitatively different between two conditions, in contrast to the dramatic on–off distinction made by the previous statement. Thus, the genetic content of a bacterium can be interpreted by a ‘gene expression machinery’ in different ways at different times. The expression of genes, and the manner in which it is regulated, have been studied in recent years using genome-scale techniques. Many of these approaches use DNA microarrays or next-generation sequencing, the basics of which have been covered in the previous two chapters. In the present, core chapter of this book, we will discuss the application of these technologies to the study of gene expression and its control in bacteria. The discussion of techniques and data analysis will focus on gene expression measurements and investigation of genomic regions that bind to a protein of interest. We will also present particular exemplary research as case studies in the process.

To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.

Four Mycoplasma arthritidis strains, two virulent (158p10p9 and 14124p10) and two avirulent (PG6 and H606), were examined for differences in their antigenic compositions. Rabbit antisera prepared against each strain were compared for their reactivities against both homologous and heterologous strains by enzyme-linked immunosorbent assay and metabolism inhibition assay. These tests confirmed a close serologic relationship among the four strains. Only by cross-absorbing each antiserum with intact cells from both homologous and heterologous strains could serologic differences be detected. Interestingly, antigenic variability was minimal among those antigens involved in the metabolism inhibition reaction, suggesting that they may be highly conserved within this species. No differences in protein composition could be detected by one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis. However, slight variation was apparent on two-dimensional gels, and antigens possessing strain-specific epitopes were detected by two-dimensional immunoblotting experiments performed with cross-absorbed antisera. The enzyme-linked immunosorbent assay, two-dimensional electrophoresis, and immunoblotting experiments showed that the two virulent strains were closely related to each other, while the avirulent strains were more distantly related to each other and to the other two strains. The relationship of M. arthritidis serologic diversity to virulence remains unclear: however, this study has demonstrated that it may be possible to subdivide M. arthritidis into serogroups on the basis of cross-absorption patterns and to identify those antigens bearing strain-specific epitopes by immunoblotting with cross-absorbed antisera.