Dataset Information

Curli biogenesis: order out of disorder.

ABSTRACT: Many bacteria assemble extracellular amyloid fibers on their cell surface. Secretion of proteins across membranes and the assembly of complex macromolecular structures must be highly coordinated to avoid the accumulation of potentially toxic intracellular protein aggregates. Extracellular amyloid fiber assembly poses an even greater threat to cellular health due to the highly aggregative nature of amyloids and the inherent toxicity of amyloid assembly intermediates. Therefore, temporal and spatial control of amyloid protein secretion is paramount. The biogenesis and assembly of the extracellular bacterial amyloid curli is an ideal system for studying how bacteria cope with the many challenges of controlled and ordered amyloid assembly. Here, we review the recent progress in the curli field that has made curli biogenesis one of the best-understood functional amyloid assembly pathways. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

SUBMITTER: Evans ML

PROVIDER: S-EPMC4243835 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Curli biogenesis: order out of disorder.

Evans Margery L ML Chapman Matthew R MR

Biochimica et biophysica acta 20130927 8

Many bacteria assemble extracellular amyloid fibers on their cell surface. Secretion of proteins across membranes and the assembly of complex macromolecular structures must be highly coordinated to avoid the accumulation of potentially toxic intracellular protein aggregates. Extracellular amyloid fiber assembly poses an even greater threat to cellular health due to the highly aggregative nature of amyloids and the inherent toxicity of amyloid assembly intermediates. Therefore, temporal and spati ...[more]

PMID: 24080089

Similar Datasets

Project description:BackgroundMore and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.ResultsThis paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties.ConclusionDistinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.

Dataset Information

Curli biogenesis: order out of disorder.

Publications

Curli biogenesis: order out of disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets