PSORT

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

Construction

PSORT consists of several Subdatenbanken from which one must first select an appropriate:

  • PSORT ( old version; bacteria, plants)
  • PSORT II (animals, yeast, and in processing plants, other Gram- positive and Gram -negative bacteria)
  • Wolf PSORT (based on PSORT II; fungi, animals, plants)
  • IPSORT (detection of N-terminal sorting signals )
  • PSORT -B ( gram-negative bacteria)

PSORT examined protein sequences using the amino acid sequences. With an initial category specifying the origin ( animal, plant, etc. ) of the protein is determined. Following the standard letter code for amino acids is entered. The output is in three sections: First, the input sequence is (possibly corrected) listed. Then the results of the subprograms are to be seen. The calculated probability of localization is done in the third section.

Result Example

Section 1: repetition of the input sequence

Section 2: Results of the subprograms (selection)

  • PSG: Signal peptide prediction

Based on sequence comparisons with the amino acid sequences fed a prediction is made ​​as to whether the input protein has a signal peptide. This is due to the length of the N-terminal region, due to the net charges in this region.

  • GvH: Signal sequence recognition

Another method for determining the signal sequence, based on the weight matrix method. In this case, the input sequences to the consensus sequences in the vicinity of possible interfaces are compared with known signal sequences.

Section 3: localization probability

Here the reserves calculated by subprograms data are aggregated by algorithms to a localization probability. Indicated are the locations with the five highest probabilities.

Strengths and weaknesses

  • The database is based on an algorithm that has been improved in 2003.
  • The entered data are not current.
  • Since 2003, the database has not improved.
  • Formal advantage is that the input sequence is automatically debugged.
  • Tip: results should only be seen as an indication of possible locations. Comparisons with localization databases that are based on experiments have shown that the prediction of PSORT does not always result in a match.
  • Exact evaluation of all partial results in PSORT provide a thorough training in the database.
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