Optimization of Plasma Sample Pretreatment for Quantitative Analysis Using iTRAQ Labeling and LC-MALDI-TOF/TOF

Shotgun proteomic methods involving iTRAQ (isobaric tags for relative and absolute quantitation) peptide labeling facilitate quantitative analyses of proteomes and searches for useful biomarkers. However, the plasma proteome''s complexity and the highly dynamic plasma protein concentration range limit the ability of conventional approaches to analyze and identify a large number of proteins, including useful biomarkers. The goal of this paper is to elucidate the best approach for plasma sample pretreatment for MS- and iTRAQ-based analyses. Here, we systematically compared four approaches, which include centrifugal ultrafiltration, SCX chromatography with fractionation, affinity depletion, and plasma without fractionation, to reduce plasma sample complexity. We generated an optimized protocol for quantitative protein analysis using iTRAQ reagents and an UltrafleXtreme (Bruker Daltonics) MALDI TOF/TOF mass spectrometer. Moreover, we used a simple, rapid, efficient, but inexpensive sample pretreatment technique that generated an optimal opportunity for biomarker discovery. We discuss the results from the four sample pretreatment approaches and conclude that SCX chromatography without affinity depletion is the best plasma sample preparation pretreatment method for proteome analysis. Using this technique, we identified 1,780 unique proteins, including 1,427 that were quantified by iTRAQ with high reproducibility and accuracy.     

Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements

Metabolic fluxes, estimated from stable isotope studies, provide a key to quantifying physiology in fields ranging from metabolic engineering to the analysis of human metabolic diseases. A serious drawback of the flux estimation method in current use is that it does not produce confidence limits for the estimated fluxes. Without this information it is difficult to interpret flux results and expand the physiological significance of flux studies. To address this shortcoming we derived analytical expressions of flux sensitivities with respect to isotope measurements and measurement errors. These tools allow the determination of local statistical properties of fluxes and relative importance of measurements. Furthermore, we developed an efficient algorithm to determine accurate flux confidence intervals and demonstrated that confidence intervals obtained with this method closely approximate true flux uncertainty. In contrast, confidence intervals approximated from local estimates of standard deviations are inappropriate due to inherent system nonlinearities. We applied these methods to analyze the statistical significance and confidence of estimated gluconeogenesis fluxes from human studies with [U-13C]glucose as tracer and found true limits for flux estimation in specific human isotopic protocols.     

PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB

Background

One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.

Results

To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.

Conclusion

PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.

     

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

Annotation of protein functions plays an important role in understanding life at the molecular level. High‐throughput sequencing produces massive numbers of raw proteins sequences and only about 1% of them have been manually annotated with functions. Experimental annotations of functions are expensive, time‐consuming and do not keep up with the rapid growth of the sequence numbers. This motivates the development of computational approaches that predict protein functions. A novel deep learning framework, DeepFunc, is proposed which accurately predicts protein functions from protein sequence‐ and network‐derived information. More precisely, DeepFunc uses a long and sparse binary vector to encode information concerning domains, families, and motifs collected from the InterPro tool that is associated with the input protein sequence. This vector is processed with two neural layers to obtain a low‐dimensional vector which is combined with topological information extracted from protein–protein interactions (PPIs) and functional linkages. The combined information is processed by a deep neural network that predicts protein functions. DeepFunc is empirically and comparatively tested on a benchmark testing dataset and the Critical Assessment of protein Function Annotation algorithms (CAFA) 3 dataset. The experimental results demonstrate that DeepFunc outperforms current methods on the testing dataset and that it secures the highest F_max = 0.54 and AUC = 0.94 on the CAFA3 dataset.     

The molecular architecture of the eukaryotic chaperonin TRiC/CCT

TRiC/CCT is a highly conserved and essential chaperonin that uses ATP cycling to facilitate folding of approximately 10% of the eukaryotic proteome. This 1 MDa hetero-oligomeric complex consists of two stacked rings of eight paralogous subunits each. Previously proposed TRiC models differ substantially in their subunit arrangements and ring register. Here, we integrate chemical crosslinking, mass spectrometry, and combinatorial modeling to reveal the definitive subunit arrangement of TRiC. In vivo disulfide mapping provided additional validation for the crosslinking-derived arrangement as the definitive TRiC topology. This subunit arrangement allowed the refinement of a structural model using existing X-ray diffraction data. The structure described here explains all available crosslink experiments, provides a rationale for previously unexplained structural features, and reveals a surprising asymmetry of charges within the chaperonin folding chamber.     

Sequence based residue depth prediction using evolutionary information and predicted secondary structure

Background  

Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.     

Impaired leptin activity in New Zealand Obese mice: model of angiogenesis

Leptin is prompt to drive angiogenesis, effecting proper vascularisation. Tissue remodeling (including adipose organ) is associated with the angiogenic response. The aim of this study was to investigate the effect of hyperleptinemia on angiogenesis in subcutaneous (s.c.) in vivo matrigel model in mice on a high fat (HF) diet. HF promoted adipose tissue accumulation and biochemical changes resembling metabolic syndrome. However, the impact of this dietary treatment on angiogenesis, measured in s.c. matrigel model was not significant. Changes in leptin concentration were not accompanied by significant angiogenic response. This lack of leptin activity and impaired signal transduction at the molecular level suggests malfunction of the leptin receptor in NZO mice.     

Human platelets release alpha-6-L-fucosyltransferase upon activation

Previously we have shown that human platelets release alpha-6-L-fucosyltransferase (EC 2.4.1.68) during coagulation of blood [(1987) Glycoconjugate J. 4, 43-49]. Here we report that agonists which induce platelet aggregation bring about release of the enzyme. In quantitative terms the release of alpha-6-L-fucosyltransferase by washed, aggregated platelets was very similar to that occurring during blood coagulation.     

Structure of 2-keto-3-deoxy-6-phosphogluconate aldolase at 2.8 Å resolution

The structure of 2-keto-3-deoxy-6-phosphogluconate aldolase has been extended to 2.8 Å resolution from 3.5 Å resolution by multiple isomorphous replacement methods using three heavy-atom derivatives and anomalous Bijvoet differences to 6 Å resolution (〈m〉 = 0.72). The replacement phases were improved and refined by electron density modification procedures coupled with inverse transform phase angle calculations. A Kendrew model of the molecule was built, which contained all 225 residues of a recently determined amino acid sequence, whereas only 173 were accounted for at 3.5 Å resolution. The missing residues were found to be part of the interior of the molecule and not simply an appendage. The molecule folds to form an eight-strand α/β-barrel structure strikingly similar to triosephosphate isomerase, the A-domain of pyruvate kinase and Taka amylase. With a knowledge of the sequence, the nature of the interfaces of the two kinds of crystallographic trimers have been examined, from which it was concluded that the choice of trimers selected in the 3.5 Å resolution work was probably correct for trimers in solution. The active site region has been established from the position of the Schiff base forming Lys144 but it has not been possible to confirm it conclusively in independent derivative experiments. An apparent anomaly exists in the location of Glu56 (about 25 Å from Lys144). The latter has been reported to assist in catalysis.