Authors: Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT.
Source: [Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.]
This method is based on a neuronal network which was trained on high resolution X-ray structures from PDB. Disordered regions are regions, which appear in the sequence record, but their electrons are missing from the electronic density map. This approach can also fail, because missing electrons can also arise because of the crystallization process.
The method runs first a PsiBlast search against a filtered sequence database. Next, a profile for each residue is calculated and classified by using the trained neuronal network.
If you run disopred on the console, you have to define the location of your database. The program needs as input your sequence in a file with fasta format.
As a prediction result you get a file with the predicted disordered region, the precision and recall. Furthermore, you can get a more detailed output. There you see the sequence, the predictions and also the probability of the prediction for each residue.
Prediction of order and disorder by machine-learning
Authors: S. Hirose, K. Shimizu, S. Kanai, Y. Kuroda and T. Noguchi
POODLE is based on a machine learning algorithm. This method is based on a 2-level SVM (Support Vector Machine).
We describe here the POODLE-L algorithm in detail, but all POODLE variants use the same principle.
The method was trained on disordered proteins and proteins with no disordered regions. On the first level, the SVM predicts the probability of a 40-residue sequence segment to be disordered. If the algorithm found such a disordered region, the second level of the SVM use the output from the first level and predicts the probability to be disordered for each amino acid.
Different variants of POODLE
Describtion: Prediction of long disorderd redions.
Source: [POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.]
Describtion: Prediction of short disorderd redions.
Source: [POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.]
Description: Integrates structural information predictors.
Source: [POODLE-I: Disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach]
Description: Compares different sequences and predicts which sequence is the most disordered one. (is not used in this analysis)
We used the [POODLE webserver] for our analysis. We paste our sequence in FASTA-format in the input window and chose the POODLE variant.
The result of this method is a file with the single amino acids, the prediction if it is ordered or not and the probability for the state. Furthermore, the method offers a graphical view of the result.
Authors: Zsuzsanna Dosztányi, Veronika Csizmók, Péter Tompa and István Simon
Source: [IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content]
IUpred calculates the pairwise energy profile along a sequence. After that the algorithm transforms the energy values into a probabilistic score, which is between 0 and 1. A score of 0 means complete order, whereas scores up to 1 mean complete disorder. The cutoff is 0.5. All residues with a score more than 0.5 are predicted as disordered.
The [Webserver] offers three different prediction methods, one focus on long disordered regions, the other method focus on short disordered regions. Furthermore, with the third method, it is possible to predict disordered regions with additional structure information.
We used the [Webserver] for our analysis. The input for the webserver is only the sequence in FASTA-format.
As an output you get a graphical representation of the prediction and a detailed list of the scores of each amino acid. Sadly, it is not possible to download the scores in a text file, so therefore you have to use the picture or to copy the data manually.
Authors:Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B
Source: [Improved Disorder Prediction by Combination of Orthogonal Approaches.]
Meta-Disorder combines different disordered prediction methods, which have different focus. Therefore, it is possible to avoid a bias to one prediction method. The combined methods are NORSnet (uses NORS for the prediction of disordered regions), PROFbval (uses mobility of the residues for prediction), Ucon (uses contacts for prediction) and DISOPRED (see above). Furthermore, Meta-Disorder also uses additional useful features like solvent accessibility or secondary structure.
We used the [Webserver ] for our prediction. The input for the server is only the amino acid sequence of the protein.
The user has the possibility to choose between different output formats. All formats are available for each prediction and the user chooses the format after the prediction and therefore, has the possibility to jump between the formats. Very useful for our purpose was the visual output, because there the user gets a nice picture of the prediction. Also very useful is the text or html output, because there is a detailed list of the different predictions, used scores and probabilities.