Sequence-based predictions HEXA

Prediction of disordered regions

• DISOPRED

Authors: Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT.

Year: 2004

Source: [Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.]

Description:

This method is based on a neuronal network which was trained on high resolution X-ray structures from PDB. Disordered regions are regions, which appears in the sequence record, but their electrons are missing from electronic density map. This approach can also failed, because missing electrons can also arise because of the cristallization 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.

Prediction:

As a prediction result you get a file with the predicted disordered region, the precision and recall. Furthermore you can a more detailed output. There you see the sequence, and the predictions and also numbers above the sequence (from 0 to 9 which shows you how likly your prediction is)

Input:

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.

• POODLE

Prediction of order and disorder by machine-learning

Authors: S. Hirose, K. Shimizu, S. Kanai, Y. Kuroda and T. Noguchi

Year: 2007

There exist three different variants of POODLE.

The first variant is called POODLE-L which predicts mainly long disorder region with a length more than 40.

Source: [POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.]

The next variant is called POODLE-S, which predicts mainly short disorder regions.

Source: [POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.]

The last variant is called POODLE-I, which integrates structal information predictors.

Source: [POODLE-I: Disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach]

There exists als another variant called POODLE-W, which compares different sequences and predicts which sequence is the most disordered one, but this method wasn't used in our analysis.

Description:

POODLE is also a machine learning based method. This method based on a 2-level SVM (Support Vector Machine).

We describe here the POODLE-L in detail, but all POODLE variants use the same principle. The method was trained on disordered proteins and proteins with no disoredered 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 regions, the second level of the SVM use the output from the first level and predicts the probability to be disordered for each amino acid.

Output:

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. Furtheremore, you get a graphical view of the result.

Input:

We used the POODLE webserver for our analysis. We paste our sequence in fasta format in the input window and chose the POODLE variant.

Prediction of GO terms

Prediction of disordered regions

• Disopred

Disopred predicts two disordered regions in our protein. The first region is at the beginning of the protein (first two residues) and the second region is at the end (last three regions). This prediction is probably wrong, because it is normal, that the electrons from the first and the last amino acids lack in the electron density map. So, our protein Hexosamidase A has no disordered regions.

Result of the Disopred prediction. * shows that this amino acid belongs to a disordered regions, whereas . signs for a non-disordered region.

• POODLE

We decided to test several POODLE variants and to compare the results.