FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Localization to the nucleolus is a common feature of coronavirus nucleoproteins and the protein may disrupt host cell division

The subcellular localization of transmissible gastroenteritis virus (TGEV) and mouse hepatitis virus (MHV) (group I and group II coronaviruses, respectively) nucleoproteins (N proteins) were examined by confocal microscopy. The proteins were shown to localize either to the cytoplasm alone or to the cytoplasm and a structure in the nucleus. This feature was confirmed to be the nucleolus by using specific antibodies to nucleolin, a major component of the nucleolus, and by confocal microscopy to image sections through a cell expressing N protein. These findings are consistent with our previous report for infectious bronchitis virus (group III coronavirus) (J. A. Hiscox et al., J. Virol. 75:506-512, 2001), indicating that nucleolar localization of the N protein is a common feature of the coronavirus family and is possibly of functional significance. Nucleolar localization signals were identified in the domain III region of the N protein from all three coronavirus groups, and this suggested that transport of N protein to the nucleus might be an active process. In addition, our results suggest that the N protein might function to disrupt cell division. Thus, we observed that approximately 30% of cells transfected with the N protein appeared to be undergoing cell division. The most likely explanation for this is that the N protein induced a cell cycle delay or arrest, most likely in the G2/M phase. In a fraction of transfected cells expressing coronavirus N proteins, we observed multinucleate cells and dividing cells with nucleoli (which are only present during interphase). These findings are consistent with the possible inhibition of cytokinesis in these cells.

Nuclear magnetic resonance structure of the N-terminal domain of nonstructural protein 3 from the severe acute respiratory syndrome coronavirus

This paper describes the structure determination of nsp3a, the N-terminal domain of the severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural protein 3. nsp3a exhibits a ubiquitin-like globular fold of residues 1 to 112 and a flexibly extended glutamic acid-rich domain of residues 113 to 183. In addition to the four beta-strands and two alpha-helices that are common to ubiquitin-like folds, the globular domain of nsp3a contains two short helices representing a feature that has not previously been observed in these proteins. Nuclear magnetic resonance chemical shift perturbations showed that these unique structural elements are involved in interactions with single-stranded RNA. Structural similarities with proteins involved in various cell-signaling pathways indicate possible roles of nsp3a in viral infection and persistence.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Apolipoprotein E genotype and alpha-tocopherol modulate amyloid precursor protein metabolism and cell cycle regulation

Apolipoprotein E4 (apoE4) genotype is associated with an increased risk for Alzheimer's disease (AD). This is thought to be in part attributable to an impact of apoE genotype on the processing of the transmembrane amyloid precursor protein (APP) thereby contributing to amyloid beta peptide formation in apoE4 carriers, which is a primary patho-physiological feature of AD. As apoE and alphato-copherol (alpha-toc) have been shown to modulate membrane bilayer properties and hippocampal gene expression, we studied the effect of apoE genotype on APP metabolism and cell cycle regulation in response to dietary a-toc. ApoE3 and apoE4 transgenic mice were fed a diet low (VE) or high (+VE) in vitamin E (3 and 235 mg alpha-toe/kg diet, respectively) for 12 weeks. Cholesterol levels and membrane fluidity were not different in synaptosomal plasma membranes isolated from brains of apoE3 and apoE4 mice (-VE and +VE). Non-amyloidogenic alpha-secretase mRNA concentration and activity were significantly higher in brains of apoE3 relative to apoE4 mice irrespective of the dietary a-toe supply, while amyloidogenic beta-secretase and gamma-secretase remained unchanged. Relative mRNA concentration of cell cycle related proteins were modulated differentially by dietary a-toc supplementation in apoE3 and apoE4 mice, suggesting genotype-dependent signalling effects on cell cycle regulation.