What determines the molecular composition of abnormal protein aggregates in neurodegenerative disease?

Abnormal protein aggregates, in the form of either extracellular plaques or intracellular inclusions, are an important pathological feature of the majority of neurodegenerative disorders. The major molecular constituents of these lesions, viz., beta-amyloid (Abeta), tau, and alpha-synuclein, have played a defining role in the diagnosis and classification of disease and in studies of pathogenesis. The molecular composition of a protein aggregate, however, is often complex and could be the direct or indirect consequence of a pathogenic gene mutation, be the result of cell degeneration, or reflect the acquisition of new substances by diffusion and molecular binding to existing proteins. This review examines the molecular composition of the major protein aggregates found in the neurodegenerative diseases including the Abeta and prion protein (PrP) plaques found in Alzheimer's disease (AD) and prion disease, respectively, and the cellular inclusions found in the tauopathies and synucleinopathies. The data suggest that the molecular constituents of a protein aggregate do not directly cause cell death but are largely the consequence of cell degeneration or are acquired during the disease process. These findings are discussed in relation to diagnosis and to studies of to disease pathogenesis.

The spatial patterns of prion protein deposits in Creutzfeldt-Jakob disease:Comparison with β-amyloid deposits in Alzheimer's disease

Similar pathological processes may be involved in the deposition of extracellular proteins in the brains of patients with Creutzfeldt-Jakob disease (CJD) and Alzheimer's disease (AD). Hence, this study compared the spatial patterns of prion protein (PrP) deposits in the cerebral cortex and hippocampus in cases of sporadic CJD with those of β-amyloid (Aβ) deposits in sporadic AD. PrP and Aβ deposits were aggregated into clusters and, in 90% of brain areas in CJD and 57% in AD, the clusters were regularly distributed parallel to the tissue boundary. In a significant proportion of cortical analyses, the mean diameter of the clusters of PrP and Aβ deposits were similar to those of the cells of origin of the cortico-cortical pathways. Aβ deposits in AD were distributed more frequently in larger-sized clusters than PrP deposits in CJD. In addition, in the hippocampus and dentate gyrus, clustering of Aβ deposits was observed in AD but PrP deposits were rare in these regions in CJD. The size, location and distribution of the extracellular protein deposits within the cortex of both disorders was consistent with the degeneration of the cortico-cortical pathways. Furthermore, spread of the pathology along these pathways may be a pathogenic feature common to CJD and AD. © 2001 Elsevier Science Ireland Ltd.

Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

Oxidized phospholipid inhibition of toll-like receptor (TLR) signaling is restricted to TLR2 and TLR4 roles for cd14, lps-binding protein, and md2 as targets for specificity of inhibition

The generation of reactive oxygen species is a central feature of inflammation that results in the oxidation of host phospholipids. Oxidized phospholipids, such as 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine (OxPAPC), have been shown to inhibit signaling induced by bacterial lipopeptide or lipopolysac-charide (LPS), yet the mechanisms responsible for the inhibition of Toll-like receptor (TLR) signaling by OxPAPC remain incompletely understood. Here, we examined the mechanisms by which OxPAPC inhibits TLR signaling induced by diverse ligands in macrophages, smooth muscle cells, and epithelial cells. OxPAPC inhibited tumor necrosis factor- production, IB degradation, p38 MAPK phosphorylation, and NF-B-dependent reporter activation induced by stimulants of TLR2 and TLR4 (Pam3CSK4 and LPS) but not by stimulants of other TLRs (poly(I·C), flagellin, loxoribine, single-stranded RNA, or CpG DNA) in macrophages and HEK-293 cells transfected with respective TLRs and significantly reduced inflammatory responses in mice injected subcutaneously or intraperitoneally with Pam3CSK4. Serum proteins, including CD14 and LPS-binding protein, were identified as key targets for the specificity of TLR inhibition as supplementation with excess serum or recombinant CD14 or LBP reversed TLR2 inhibition by OxPAPC, whereas serum accessory proteins or expression of membrane CD14 potentiated signaling via TLR2 and TLR4 but not other TLRs. Binding experiments and functional assays identified MD2 as a novel additional target of OxPAPC inhibition of LPS signaling. Synthetic phospholipid oxidation products 1-palmitoyl-2-(5-oxovaleryl)-sn-glycero-3-phosphocholine and 1-palmitoyl-2-glutaryl-sn-glycero-3-phosphocholine inhibited TLR2 signaling from 30 µM. Taken together, these results suggest that oxidized phospholipid-mediated inhibition of TLR signaling occurs mainly by competitive interaction with accessory proteins that interact directly with bacterial lipids to promote signaling via TLR2 or TLR4.

Analysis of the pore of the unusual major intrinsic protein channel, yeast Fps1p

Fps1p is a glycerol efflux channel from Saccharomyces cerevisiae. In this atypical major intrinsic protein neither of the signature NPA motifs of the family, which are part of the pore, is preserved. To understand the functional consequences of this feature, we analyzed the pseudo-NPA motifs of Fps1p by site-directed mutagenesis and assayed the resultant mutant proteins in vivo. In addition, we took advantage of the fact that the closest bacterial homolog of Fps1p, Escherichia coli GlpF, can be functionally expressed in yeast, thus enabling the analysis in yeast cells of mutations that make this typical major intrinsic protein more similar to Fps1p. We observed that mutations made in Fps1p to "restore" the signature NPA motifs did not substantially affect channel function. In contrast, when GlpF was mutated to resemble Fps1p, all mutants had reduced activity compared with wild type. We rationalized these data by constructing models of one GlpF mutant and of the transmembrane core of Fps1p. Our model predicts that the pore of Fps1p is more flexible than that of GlpF. We discuss the fact that this may accommodate the divergent NPA motifs of Fps1p and that the different pore structures of Fps1p and GlpF may reflect the physiological roles of the two glycerol facilitators.

Immunological protein-protein interactions:a critical reflection

Life, and the biochemistry of which it is ultimately comprised, is built from the interactions of proteins, and the study of protein-protein interactions is fast becoming a central feature of molecular bioscience. This is as true of immunobiology as it is of other areas of the wider biological milieu. Protein-protein interactions within an immunological setting comprise both the kind familiar from other areas of biology and instantiations of protein-protein interactions special to the immune arena. Of the generic kind of protein-protein interaction, co-stimulatory receptors, such as CD28, and the interaction of accessory proteins, such as CD4 or CD8, are amongst the most prevalent and apposite of examples. The key examples of special immunological instantiations of protein-protein interactions are the binding of antigens by antibodies and the formation of peptide-MHC-TCR complexes; both prime examples of vital molecular recognition events mediated by protein-protein interactions. In this brief review, and within the context of this burgeoning field, we examine immunological protein-protein interactions, focussing on the problematic nature of defining such interactions. © 2011 by Nova Science Publishers, Inc. All rights reserved.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.