Protein aggregation predictors

Amyloidogenic pattern

Submissions are scanned for the existence of this pattern {P}-{PKRHW}-[VLSCWFNQE]-[ILTYWFNE]-[FIY]-{PKRH} at identity level, with the use of a simple custom script.

Based on physico-chemical principles of secondary structure formation extended by the assumption that the core regions of an aggregate are fully buried.

Relates average packing density of residues to the formation of amyloid fibrils.

Prediction of B-strand propensity score to locate in the amyloid fibril.

Hexapeptides of a submitted protein are threaded onto over 2500 templates of microcrystallic structure of NNQQNY, energy values below -27.00 are considered as hits.

Prediction of 'aggregation-prone' in protein sequences, based on an aggregation propensity scale for natural amino acids derived from in vivo experiments.

Prediction of the aggregation propensities single or multiple sequences based on physicochemical properties.

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Identification of Hexapeptides associated to amyloid fibrillar aggregates.

AMYLPRED2

Quantification of the influence of the tertiary interation on secondary structural preference.

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Predict the probability that particular portions of a protein will form amyloid.

Prediction of amyloid regions using expected probability of hydrogen bonds formation and packing densitites of residues.

AMYLPRED2

Application of position-specific substitution matrices (PSSM) obtained from amyloidogenic peptides.

Structure based prediction of fribrillation propoensities, using crystal strucutrue of the fibril forming peptide NNQQNY from the sup 35 prion protein of Saccharomyces cerevisiae.

Amyloidogenic patterns, average packing density, beta-strand contiguity, pafig, Net-CSSP, STITCHER

Amyloidogenic patterns, average packing density, beta-strand contiguity, pafig, Net-CSSP, STITCHER

Predicts the most aggregation-prone portions and the corresponding β-strand inter-molecular pairing for multiple input sequences.

Identification of amyloid forming peptides and amorphous peptides using a dataset of 139 amyloids and 168 amorphous peptides.

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.

Based on an assumption that protein sequences that are able to form β-arcades are amyloidogenic.

Sequence-based method of predicting protein solubility and generic aggregation propensity.

AmyloGram predicts amyloid proteins using n-gram encoding and random forests.

Reconstruction of amyloid structures containing adjacent β-arches.

Method that uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins.

Predict changes in aggregation rate upon point mutations

Identification amyloid-fibril forming peptides and regions in protein sequences