NMR assignment of a KlbA intein precursor from Methanococcus jannaschii

The backbone and side chain resonance assignments of a precursor of the KlbA intein from Methanococcus jannaschii have been determined, based on triple-resonance experiments with the uniformly [¹³C,¹⁵N]-labeled protein.     

1H, 13C, and 15N resonance assignment of the cAMP-regulated phosphoprotein endosulfine-alpha in free and micelle-bound states

¹³C, ¹⁵N, and ¹H chemical shift assignments are presented for the cAMP-regulated phosphoprotein endosulfine-alpha in its free and micelle-bound states. Secondary chemical shift analysis demonstrates formation of four helices in the micelle-bound state, which are not present in the absence of detergent.     

Exploring proteasome complexes by proteomic approaches

The ubiquitin proteasome system (UPS) represents a major pathway for intracellular protein degradation. Proteasome dependent protein quality control participates in cell cycle, immune response and apoptosis. Therefore, the UPS is in focus of therapeutic investigations and the development of pharmaceutical agents. Detailed analyses on proteasome structure and function are the foundation for drug development and clinical studies. Proteomic approaches contributed significantly to our current knowledge in proteasome research. In particular, 2-DE has been essential in facilitating the development of current models on molecular composition and assembly of proteasome complexes. Furthermore, developments in MS enabled identification of UPS proteins and their PTMs at high accuracy and high-throughput. First results on global characterization of the UPS are also available. Although the UPS has been intensively investigated within the last two decades, its functional significance and contribution to the regulation of cell and tissue phenotypes remain to be explored. This review recapitulates a variety of applied proteomic approaches in proteasome exploration, and presents an overview of current technologies and their potential in driving further investigations.     

Light-induced immobilisation of biomolecules as an attractive alternative to microdroplet dispensing-based arraying technologies

The present work shows how UV 'light-induced molecular immobilisation' (LIMI) of biomolecules onto thiol reactive surfaces can be used to make biosensors, without the need for traditional microdispensing technologies. Using 'LIMI,' arrays of biomolecules can be created with a high degree of reproducibility. This technology can be used to circumvent the need for often expensive nano/microdispensing technologies. The ultimate size of the immobilised spots is defined by the focal area of the UV beam, which for a diffraction-limited beam can be less than 1 microm in diameter. LIMI has the added benefit that the immobilised molecules will be spatially oriented and covalently bound to the surface. The activity of the sensor molecules is retained. Antibody sensor arrays made using LIMI demonstrated successful antigen binding. In addition, the pattern of immobilised molecules on the surface is not restricted to conventional array formats. The ultimate consequence of the LIMI is that it is possible to write complex protein patterns using bitmaps at high resolution onto substrates. Thus, LIMI of biomolecules provides a new technological platform for biomolecular immobilisation and the potential for replacing present microdispensing arraying technologies.     

Learning the drug target-likeness of a protein

Current drug discovery and development approaches rely extensively on the identification and validation of appropriate targets; for example, those with marketable and robust therapeutics. Wide-ranging efforts have been directed at this problem and various approaches have been developed to identify disease-associated genes as candidates. In this work, we show with statistical significance that successful drug targets, in addition to their linkage to disease, share common characteristics that are disease-independent. For example, marked differences in functional category, tissue specificity, and sequence variability are observed between known targets and average proteins. These results lead to an interesting hypothesis: potentially good drug targets shall have some desired properties, which we refer to as "drug target-likeness" that are beyond their disease-associations. Because of the limited availability of comprehensive protein characteristics data, we tried to learn the drug target-likeness property at the sequence level. Results show that a support vector machine model is able to accurately distinguish targets from nontargets entirely with sequence features. It is our hope that these encouraging results will invite future systematic proteomic scale experiments to gather necessary protein characteristics data for the accurate and predictive definition of "drug target-likeness", providing a new perspective toward understanding and pursuing effective therapeutics.     

Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data

This study assesses the ability of a novel family of machine learning algorithms to identify changes in relative protein expression levels, measured using 2-D DIGE data, which support accurate class prediction. The analysis was done using a training set of 36 total cellular lysates comprised of six normal and three cancer biological replicates (the remaining are technical replicates) and a validation set of four normal and two cancer samples. Protein samples were separated by 2-D DIGE and expression was quantified using DeCyder-2D Differential Analysis Software. The relative expression reversal (RER) classifier correctly classified 9/9 training biological samples (p<0.022) as estimated using a modified version of leave one out cross validation and 6/6 validation samples. The classification rule involved comparison of expression levels for a single pair of protein spots, tropomyosin isoforms and alpha-enolase, both of which have prior association as potential biomarkers in cancer. The data was also analyzed using algorithms similar to those found in the extended data analysis package of DeCyder software. We propose that by accounting for sources of within- and between-gel variation, RER classifiers applied to 2-D DIGE data provide a useful approach for identifying biomarkers that discriminate among protein samples of interest.     

Antibody microarray-based profiling of complex specimens: systematic evaluation of labeling strategies

Antibody microarrays have often had limited success in detection of low abundant proteins in complex specimens. Signal amplification systems improve this situation, but still are quite laborious and expensive. However, the issue of sensitivity is more likely a matter of kinetically appropriate microarray design as demonstrated previously. Hence, we re-examined in this study the suitability of simple and inexpensive detection approaches for highly sensitive antibody microarray analysis. N-hydroxysuccinimidyl ester (NHS)- and Universal Linkage System (ULS)-based fluorescein and biotin labels used as tags for subsequent detection with anti-fluorescein and extravidin, respectively, as well as fluorescent dyes were applied for analysis of blood plasma. Parameters modifying strongly the performance of microarray detection such as labeling conditions, incubation time, concentrations of anti-fluorescein and extravidin and extent of protein labeling were analyzed and optimized in this study. Indirect detection strategies whether based on NHS- or ULS-chemistries strongly outperformed direct fluorescent labeling and enabled detection of low abundant cytokines with many dozen-fold signal-to-noise ratios. Finally, particularly sensitive detection chemistry was applied to monitoring cytokine production of stimulated peripheral T cells. Microarray data were in accord with quantitative cytokine levels measured by ELISA and Luminex, demonstrating comparable reliability and femtomolar range sensitivity of the established microarray approach.     

Prediction of Protein-Protein Interactions Using Protein Signature Profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Inactivation of the Hansenula polymorpha PMR1 gene affects cell viability and functioning of the secretory pathway

In yeast, functions of the endoplasmic reticulum (ER) depend on the Golgi apparatus Ca2+ pool, which is replenished by the medial-Golgi ion pump Pmr1p. Here, to dissect the role of the Golgi Ca2+ pool in protein folding and elimination of unfolded proteins in the ER, the manifestations of the pmr1 mutation in yeast Hansenula polymorpha were studied. The PMR1 gene was disrupted in a H. polymorpha diploid strain. Haploid segregants of this diploid bearing the disruption allele were viable, though they showed a severe growth defect on synthetic medium and rapidly died during storage at low temperature. Disruption of H. polymorpha PMR1 led to defects of the Golgi-hosted protein glycosylation and vacuolar protein sorting. This mutation increased the survival rate of H. polymorpha cells upon treatment with the proapoptotic drug amiodarone. Unlike Saccharomyces cerevisiae, the H. polymorpha pmr1 mutant was not hypersensitive to chemicals that induce the accumulation of unfolded proteins in the ER, indicating that the elimination of unfolded proteins from the ER was not essentially affected. At the same time, the pmr1 mutation improved the secretion of human urokinase and decreased its intracellular aggregation, indicating an influence of the mutation on the protein folding in the ER.