Predicts the aggregation propensities of several disease-related mutations in the Alzheimers b-peptide. TANGO is based on simple physico-chemical principles of secondary structure formation extended by the assumption that the core regions of an aggregate are fully buried. This method offers the possibility to screen large databases for potentially disease-related aggregation motifs as well as to optimize recombinant protein yields by rationally out-designing protein aggregation.
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This web app helps to predict the aggregation propensities of polypeptide chains based on their amino acid sequences. Zyggregator uses the fact that the sequence of a protein determines its behaviour in the case of folding, misfolding and aggregation. This tool is of significant value in developing rational approaches to the avoidance of aggregation in biotechnology and to the treatment of protein deposition diseases.
A web app which uses a position-specific scoring matrix to determine amyloid-forming sequences. Waltz allows users to identify and better distinguish between amyloid sequences and amorphous beta-sheet aggregates and allowed us to identify amyloid-forming regions in functional amyloids.
Determines protein aggregation from sequence. PASTA can evaluate the effect of point-mutations on the aggregation profile. It annotates each sequence with aggregation-prone regions, alpha-helix, beta-strand, coil and intrinsic disordered regions. This tool is able to identify secondary structure using a machine learning algorithm. It enables the study of pathogenic mutations and of cross-amyloid interactions between protein heterodimers.
Allows searching candidates (“candies”) capable of forming stacks of β-arches. ArchCandy can explore the human proteome (over 21 000) and uses positive (composed of 23 proteins and peptides known) and negative data sets. This tool predicts amyloid formation in solution and doesn’t take into consideration the interactions with other surfaces. It identifies about 85% and 94% of proteins in the extended positive and negative data sets.
A server for prediction of aggregation propensity in protein structures and rational design of protein solubility. In dynamic mode, A3D takes into account the flexibility of natural and designed polypeptides.
Applications in predicting non-native secondary structures and amyloid fibril formation.
A simple and fast algorithm that predicts aggregation-prone segments in protein sequences, compares the aggregation properties of different proteins or protein sets and analyses the effect of mutations on protein aggregation propensities.
Prediction of amyloidogenic regions in the protein chain.
Predicts the propensity of a protein to be soluble upon overexpression in E. coli using a two-stage SVM architecture based on multiple representations of the primary sequence. Each classifier of the first layer takes as input a distinct set of features describing the sequence. A final SVM classifier summarizes the resulting predictions and predicts if the protein is soluble or not as well as the corresponding probability.
Collects the amyloidogenic sequences from all major sources. AmyLoad allows for filtration of the fragments and provides detailed information about each of them. Registered users can both personalize their work with the website and submit their own sequences into the database. To maintain database reliability, submitted sequences are reviewed before making them available to the public.
Discriminating between Amyloid forming peptide and Amorphous Beta Aggregate.
Allows reconstruction of s β-serpentine arrangements from individual β-arches. BetaSerpentine predicts possible β-serpentine arrangements of adjacent β-arches and ranks them based on the newly developed scores in order of preference. The purpose of this tool is reconstruction of all possible β-serpentine arrangements within an analyzed protein sequence. User can specify two thresholds: (1) for the ArchCandy scores of individual β-arches that are used for β-serpentines construction, and (2) for the general β-serpentine score.
Automates the process of carefully selecting point mutations that minimize the intrinsic aggregation propensity whilst improving local protein stability. This plugin combines TANGO, FoldX and YASARA.
Identifies molecules that are known to aggregate or may aggregate in biochemical assays. Aggregator Advisor allows to ask whether a molecule is already known to aggregate and offers guidance as to whether the molecule is likely to do it, and what controls may be undertaken. This method can be used interactively via its web site, or the entire database can be downloaded for interrogation.
Calculates likelihood scores for potential beta-strands and strand-pairs based on correlations observed in parallel beta-sheets.
Estimates mass per unit length (MPL) for fibrilar biological assemblies. MpUL-multi is a standalone software, developed for rapid MPL estimation, allowing for the semi-automated processing of tilted beam transmission electron microscopy (TB-TEM) data from dark field imaging through a graphical interface. The application is able to average values from multiple standards to reduce variability due to differences in the intensity measured from Tobacco Mosaic Virus (TMV) molecules.
Predicts amyloidogenic sequences in eukaryotic proteins using random forests and n-gram analysis. AmyloGram was developed for the detection of amyloid peptides or amyloidogenic regions using an external data set. It can predict amyloidogenic but not amyloidic regions. This software is available as the web-server and as the R package.
It employs a consensus of different methods that have been found or specifically developed to predict features related to the formation of amyloid fibrils.
A meta-predictor for hot spots prediction for METapredictor for AMYLoid proteins.
An amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation. APPNN allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea).
A machine learning method for classification of aminoacid sequences, based on discovering a segment with a discriminative pattern of site-specific co-occurrences between sequence elements.
A method based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates.
Generates structural models for any amyloid fibril, starting from its sequence. Fibpredictor is a computational procedure that identifies energetically favorable class of amyloid fibril for a peptide sequence, or whether equally favorable amyloid fibril structures for the same sequence exist. Results are combined with experimental data to determine the sense of amyloid b-spine to reduce the analysis to a small subset of fibril classes. In such cases, Fibpredictor demonstrated the ability to identify the correct amyloid fibril structures among top-ranked conformations
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Serves for downstream analysis of MaxQuant output. Proteus is a program that allows peptide and protein aggregation, quality checks, data exploration and visualisation. It can be used for studying the properties of individual proteins in the context of the interactive volcano or fold-change-intensity plot. A basic data processing flow in Proteus is as follows: read the evidence data and metadata, aggregate peptides and proteins, normalize protein data, perform differential expression and explore the results.
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