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Table 1 Description of disorder predictors analyzed in this work

From: MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins

Method

Short description

Availability

Ref.

DisEMBL

ANN trained to predict classic loops (DSSP), flexible loops with high B-factors, missing coordinates in X-ray structures, regions of low-complexity and prone to aggregation.

local installation

[21]

DISOPRED2

SVM trained to predict residues with missing coordinates.

local installation

[22]

DISpro

Recursive neural networks (RNNs) trained to predict missing coordinates.

local installation

[23]

GlobPlot

A simple method based on several hydrophobicity scales to predict regions of missing coordinates and loops with high B-factors.

local installation

[24]

iPDA

Incorporates information about sequence conservation, predicted secondary structure, sequence complexity and hydrophobic clusters.

web service

[25]

IUPred

Estimates pairwise interaction energies using a statistical potential. Two versions for predicting long and short disorder.

web service

[26]

Pdisorder

Combination of neural network, linear discriminant function and acute smoothing procedure is used for recognition of disordered and ordered regions in proteins.

web service

[27]

Poodle-s

SVM trained for short disorder detection (uses PSSMs generated by PSI-BLAST).

web service

[28]

Poodle-l

Predicts long disorder using an SVM.

web service

[29]

PrDOS

Predicts missing coordinates in 3D structure using SVM and PSSMs from PSI-BLAST.

web service

[30]

Spritz

Predicts long and short disorder (missing coordinates) using two separate SVMs. Utilizes secondary structure.

web service

[31]

RONN

Predicts missing coordinates using an ANN.

local installation

[33]