PSORT

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PSORT is a database that predicts protein locations in cells. This is done by analyzing the amino acid sequence entered. Sequences which are characteristic of sorting signals (for example length of the N-terminal region , the hydrophobic region , net charges ) are compared with known sorting signals. A localization probability for the cytoplasm , periplasm , inner membrane and outer membrane is calculated from this.

construction

PSORT consists of several sub-databases from which one must first select a suitable one:

  • PSORT (old version; bacteria, plants)
  • PSORT II (animals, yeast; in progress: plants, gram-positive and gram-negative bacteria)
  • Wolf PSORT (based on PSORT II; mushrooms, animals, plants)
  • iPSORT (detection of N-terminal sorting signals)
  • PSORT-B (Gram-negative bacteria)

PSORT examines protein sequences using the amino acid sequences. The origin (animal, plant, etc.) of the protein is determined with an initial category determination. The standard letter code for amino acids is then entered. The output takes place in three sections: First, the entered sequence is listed (corrected if necessary). The results of the sub-programs can then be seen. The calculated probability of localization takes place in the third section.

Result example

Section 1: Repetition of the input sequence

Section 2 results of the sub-programs (selection)

  • PSG: signal peptide prediction

On the basis of sequence comparisons with amino acid sequences fed in, a prediction is made as to whether the protein fed in has a signal peptide . This happens because of the length of the N-terminal region and because of the net charges in that region.

  • GvH: signal sequence recognition

Another method for determining signal sequences based on the weight matrix method . The input sequences at the consensus sequences in the vicinity of the possible interfaces are compared with known signal sequences.

Section 3: Localization Probability

Here, the data calculated by the subprograms are added to a localization probability using algorithms. The locations with the five highest probabilities are given.

Strengths and weaknesses

  • The database is based on an algorithm that was improved in 2003.
  • The fed-in data is not up-to-date.
  • The database has not been improved since 2003.
  • Formal advantage: The input sequence is automatically cleared of errors.
  • Tip: Results should only be viewed as an indication of possible localizations. Comparisons with location databases based on experiments have shown that the prediction of PSORT does not always lead to a match.
  • Exact evaluation of all partial results in PSORT requires thorough familiarization with the database.

Web links