Dataset Information

The on-fibrillation-pathway membrane content leakage and off-fibrillation-pathway lipid mixing induced by 40-residue ?-amyloid peptides in biologically relevant model liposomes.

ABSTRACT: Disruption of the synaptic plasma membrane (SPM) induced by the aggregation of ?-amyloid (A?) peptides has been considered as a potential mechanism for the neurotoxicity of A? in Alzheimer's disease (AD). However, the molecular basis of such membrane disruption process remains unclear, mainly because of the severe systematic heterogeneity problem that prevents the high-resolution studies. Our previous studies using a two-component phosphatidylcholine (PC)/phosphatidylglycerol (PG) model liposome showed the presence of A?-induced membrane disruptions that were either on the pathway or off the pathway of fibril formation. The present study focuses on a more biologically relevant model membrane with compositions that mimic the outer leaflet of SPMs. The main findings are: (1) the two competing membrane disruption effects discovered in PC/PG liposomes and their general peptide-to-lipid-molar-ratio dependence persist in the more complicated membrane models; (2) the SPM-mimic membrane promotes the formation of certain "on-fibrillation-pathway" intermediates with higher ?-helical structural population, which lead to more rapid and significant of membrane content leakage; (3) although the "on-fibrillation-pathway" intermediate structures show dependence on membrane compositions, there seems to be a common final fibril structure grown from different liposomes, suggesting that there may be a predominant fibril structure for 40-residue A? (i.e. A?40) peptides in biologically-relevant membranes. This article is part of a Special Issue entitled: Protein Aggregation and Misfolding at the Cell Membrane Interface edited by Ayyalusamy Ramamoorthy.

SUBMITTER: Cheng Q

PROVIDER: S-EPMC6295276 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The on-fibrillation-pathway membrane content leakage and off-fibrillation-pathway lipid mixing induced by 40-residue β-amyloid peptides in biologically relevant model liposomes.

Cheng Qinghui Q Hu Zhi-Wen ZW Doherty Katelynne E KE Tobin-Miyaji Yuto J YJ Qiang Wei W

Biochimica et biophysica acta. Biomembranes 20180313 9

Disruption of the synaptic plasma membrane (SPM) induced by the aggregation of β-amyloid (Aβ) peptides has been considered as a potential mechanism for the neurotoxicity of Aβ in Alzheimer's disease (AD). However, the molecular basis of such membrane disruption process remains unclear, mainly because of the severe systematic heterogeneity problem that prevents the high-resolution studies. Our previous studies using a two-component phosphatidylcholine (PC)/phosphatidylglycerol (PG) model liposome ...[more]

PMID: 29548698

Similar Datasets

Project description:BackgroundThe use of 3-D similarity techniques in the analysis of biological data and virtual screening is pervasive, but what is a biologically meaningful 3-D similarity value? Can one find statistically significant separation between "active/active" and "active/inactive" spaces? These questions are explored using 734,486 biologically tested chemical structures, 1,389 biological assay data sets, and six different 3-D similarity types utilized by PubChem analysis tools.ResultsThe similarity value distributions of 269.7 billion unique conformer pairs from 734,486 biologically tested compounds (all-against-all) from PubChem were utilized to help work towards an answer to the question: what is a biologically meaningful 3-D similarity score? The average and standard deviation for the six similarity measures STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt were 0.54 ± 0.10, 0.07 ± 0.05, 0.62 ± 0.13, 0.41 ± 0.11, 0.18 ± 0.06, and 0.59 ± 0.14, respectively. Considering that this random distribution of biologically tested compounds was constructed using a single theoretical conformer per compound (the "default" conformer provided by PubChem), further study may be necessary using multiple diverse conformers per compound; however, given the breadth of the compound set, the single conformer per compound results may still apply to the case of multi-conformer per compound 3-D similarity value distributions. As such, this work is a critical step, covering a very wide corpus of chemical structures and biological assays, creating a statistical framework to build upon.The second part of this study explored the question of whether it was possible to realize a statistically meaningful 3-D similarity value separation between reputed biological assay "inactives" and "actives". Using the terminology of noninactive-noninactive (NN) pairs and the noninactive-inactive (NI) pairs to represent comparison of the "active/active" and "active/inactive" spaces, respectively, each of the 1,389 biological assays was examined by their 3-D similarity score differences between the NN and NI pairs and analyzed across all assays and by assay category types. While a consistent trend of separation was observed, this result was not statistically unambiguous after considering the respective standard deviations. While not all "actives" in a biological assay are amenable to this type of analysis, e.g., due to different mechanisms of action or binding configurations, the ambiguous separation may also be due to employing a single conformer per compound in this study. With that said, there were a subset of biological assays where a clear separation between the NN and NI pairs found. In addition, use of combo Tanimoto (ComboT) alone, independent of superposition optimization type, appears to be the most efficient 3-D score type in identifying these cases.ConclusionThis study provides a statistical guideline for analyzing biological assay data in terms of 3-D similarity and PubChem structure-activity analysis tools. When using a single conformer per compound, a relatively small number of assays appear to be able to separate "active/active" space from "active/inactive" space.

Project description:BACKGROUND:Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. RESULTS:In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners. CONCLUSIONS:An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.

Dataset Information

The on-fibrillation-pathway membrane content leakage and off-fibrillation-pathway lipid mixing induced by 40-residue ?-amyloid peptides in biologically relevant model liposomes.

Publications

The on-fibrillation-pathway membrane content leakage and off-fibrillation-pathway lipid mixing induced by 40-residue β-amyloid peptides in biologically relevant model liposomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets