Dataset Information

Amyloidogenic determinants are usually not buried.

ABSTRACT: BACKGROUND: Amyloidoses are a group of usually fatal diseases, probably caused by protein misfolding and subsequent aggregation into amyloid fibrillar deposits. The mechanisms involved in amyloid fibril formation are largely unknown and are the subject of current, intensive research. In an attempt to identify possible amyloidogenic regions in proteins for further experimental investigation, we have developed and present here a publicly available online tool that utilizes five different and independently published methods, to form a consensus prediction of amyloidogenic regions in proteins, using only protein primary structure data. RESULTS: It appears that the consensus prediction tool is slightly more objective than individual prediction methods alone and suggests several previously not identified amino acid stretches as potential amyloidogenic determinants, which (although several of them may be overpredictions) require further experimental studies. The tool is available at: http://biophysics.biol.uoa.gr/AMYLPRED. Utilizing molecular graphics programs, like O and PyMOL, as well as the algorithm DSSP, it was found that nearly all experimentally verified amyloidogenic determinants (short peptide stretches favouring aggregation and subsequent amyloid formation), and several predicted, with the aid of the tool AMYLPRED, but not experimentally verified amyloidogenic determinants, are located on the surface of the relevant amyloidogenic proteins. This finding may be important in efforts directed towards inhibiting amyloid fibril formation. CONCLUSION: The most significant result of this work is the observation that virtually all, to date, experimentally determined amyloidogenic determinants and the majority of predicted, but not yet experimentally verified short amyloidogenic stretches, lie 'exposed' on the surface of the relevant amyloidogenic proteins, and also several of them have the ability to act as conformational 'switches'. Experiments, focused on these fragments, should be performed to test this idea.

SUBMITTER: Frousios KK

PROVIDER: S-EPMC2714319 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Amyloidogenic determinants are usually not buried.

Frousios Kimon K KK Iconomidou Vassiliki A VA Karletidi Carolina-Maria CM Hamodrakas Stavros J SJ

BMC structural biology 20090709

<h4>Background</h4>Amyloidoses are a group of usually fatal diseases, probably caused by protein misfolding and subsequent aggregation into amyloid fibrillar deposits. The mechanisms involved in amyloid fibril formation are largely unknown and are the subject of current, intensive research. In an attempt to identify possible amyloidogenic regions in proteins for further experimental investigation, we have developed and present here a publicly available online tool that utilizes five different an ...[more]

PMID: 19589171

Similar Datasets

Project description:The advancement in site-directed spin labeling of proteins has enabled EPR studies to expand into newer research areas within the umbrella of protein-membrane interactions. Recently, membrane remodeling by amyloidogenic and non-amyloidogenic proteins has gained a substantial interest in relation to driving and controlling vital cellular processes such as endocytosis, exocytosis, shaping of organelles like endoplasmic reticulum, Golgi and mitochondria, intracellular vesicular trafficking, formation of filopedia and multivesicular bodies, mitochondrial fusion and fission, and synaptic vesicle fusion and recycling in neurotransmission. Misregulation in any of these processes due to an aberrant protein (mutation or misfolding) or alteration of lipid metabolism can be detrimental to the cell and cause disease. Dissection of the structural basis of membrane remodeling by proteins is thus quite necessary for an understanding of the underlying mechanisms, but it remains a formidable task due to the difficulties of various common biophysical tools in monitoring the dynamic process of membrane binding and bending by proteins. This is largely since membranes generally complicate protein structure analysis and this problem is amplified for structural analysis in the presence of different types of membrane curvatures. Recent EPR studies on membrane remodeling by proteins show that a significant structural information can be generated to delineate the role of different protein modules, domains and individual amino acids in the generation of membrane curvature. These studies also show how EPR can complement the data obtained by high resolution techniques such as X-ray and NMR. This perspective covers the application of EPR in recent studies for understanding membrane remodeling by amyloidogenic and non-amyloidogenic proteins that is useful for researchers interested in using or complimenting EPR to gain better understanding of membrane remodeling. We also discuss how a single protein can generate different type of membrane curvatures using specific conformations for specific membrane structures and how EPR is a versatile tool well-suited to analyze subtle alterations in structures under such modifying conditions which otherwise would have been difficult using other biophysical tools.

Project description:BACKGROUND: Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. RESULTS: Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. CONCLUSION: A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status - amyloidogenic or non-amyloidogenic. The proposed approach achieves acceptable results, comparable to most online predictors. Besides, it compensates the lacuna in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity.

Dataset Information

Amyloidogenic determinants are usually not buried.

Publications

Amyloidogenic determinants are usually not buried.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets