PSORT

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Contents

Description

PSORT is a family of programs for subcellular localization prediction as well as other datasets and resources relevant to localization prediction.

  • PSORT [1] for plant sequences and gram negative bacterial sequences
  • PSORT-B [2] and PSORTb [3] for bacterial sequences
  • PSORT II [4] and WoLF PSORT [5] for eukaryotic sequences
  • iPSORT [6] for classification of eukaryotic N-terminal sorting signals

Website

http://www.psort.org/

Examples

PSORT

PSORT --- Prediction of Protein Localization Sites version 6.4(WWW)
MY_PROTEIN_SEQ          404 Residues
Species classification: 2

*** Reasoning Step: 1

[1]Lipop: Examining lipoprotein consensus (Klein et al.:modified)
     Possible modific. site: -1  CRend: 1
[2]McG: Examining signal sequence (McGeoch)
     Length  of  UR:   13
     Peak Value of UR:   1.09
     Net Charge of CR: 1
     Discriminant Score:     -5.97
[3]GvH: Examining signal sequence (von Heijne)
     Signal Score (-7.5): -1.23
     Possible cleavage site: 27
>>> Seems to have no N-terminal signal seq.
[4]Amino Acid Composition of Predicted Mature Form:
   calculated from 1
[5]ALOM: Finding transmembrane regions (Klein et al.)
     count: 11  value: -10.56 threshold:   0.0
     INTEGRAL    Likelihood =-10.56   Transmembrane  234 - 250 ( 229 - 261)
     INTEGRAL    Likelihood = -9.45   Transmembrane  112 - 128 (  99 - 147)
     INTEGRAL    Likelihood = -8.92   Transmembrane  374 - 390 ( 369 - 392)
     INTEGRAL    Likelihood = -5.10   Transmembrane  129 - 145 ( 129 - 147)
     INTEGRAL    Likelihood = -4.57   Transmembrane  279 - 295 ( 278 - 301)
     INTEGRAL    Likelihood = -3.93   Transmembrane  340 - 356 ( 339 - 358)
     INTEGRAL    Likelihood = -2.39   Transmembrane  312 - 328 ( 307 - 329)
     INTEGRAL    Likelihood = -1.91   Transmembrane   46 -  62 (  46 -  65)
     INTEGRAL    Likelihood = -1.86   Transmembrane  195 - 211 ( 195 - 212)
     INTEGRAL    Likelihood = -1.49   Transmembrane  252 - 268 ( 252 - 268)
     INTEGRAL    Likelihood = -0.80   Transmembrane   18 -  34 (  18 -  34)
     PERIPHERAL  Likelihood =  0.21
    modified ALOM score:   2.61
Rule: cytoplasmic membrane protein

*** Reasoning Step: 2

----- Final Results -----

         bacterial inner membrane --- Certainty= 0.522(Affirmative) < succ>
      bacterial periplasmic space --- Certainty= 0.000(Not Clear) < succ>
         bacterial outer membrane --- Certainty= 0.000(Not Clear) < succ>
              bacterial cytoplasm --- Certainty= 0.000(Not Clear) < succ>

----- The End -----

PSORT-B

PSORT-B Results

SeqID: MY_PROTEIN_SEQ
  Analysis Report:
    CMSVM-            CytoplasmicMembrane           [No details]
    CytoSVM-          Unknown                       [No details]
    ECSVM-            Unknown                       [No details]
    HMMTOP-           CytoplasmicMembrane           [13 internal helices found]
    Motif-            Unknown                       [No motifs found]
    OMPMotif-         Unknown                       [No motifs found]
    OMSVM-            Unknown                       [No details]
    PPSVM-            Unknown                       [No details]
    Profile-          Unknown                       [No matches to profiles found]
    SCL-BLAST-        Unknown                       [No matches against database]
    SCL-BLASTe-       Unknown                       [No matches against database]
    Signal-           Unknown                       [No signal peptide detected]
  Localization Scores:
    Cytoplasmic            0.00
    CytoplasmicMembrane    10.00
    Periplasmic            0.00
    OuterMembrane          0.00
    Extracellular          0.00
  Final Prediction:
    CytoplasmicMembrane    10.00

References

  1. Nakai K and Kanehisa M. Expert system for predicting protein localization sites in gram-negative bacteria. Proteins. 1991;11(2):95-110. DOI:10.1002/prot.340110203 | PubMed ID:1946347 | HubMed [PSORT]
  2. Gardy JL, Spencer C, Wang K, Ester M, Tusnády GE, Simon I, Hua S, deFays K, Lambert C, Nakai K, and Brinkman FS. PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res. 2003 Jul 1;31(13):3613-7. PubMed ID:12824378 | HubMed [PSORTb2003]
  3. Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, Ester M, and Brinkman FS. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics. 2005 Mar 1;21(5):617-23. DOI:10.1093/bioinformatics/bti057 | PubMed ID:15501914 | HubMed [PSORTb2004]
  4. Horton P and Nakai K. Better prediction of protein cellular localization sites with the k nearest neighbors classifier. Proc Int Conf Intell Syst Mol Biol. 1997;5:147-52. PubMed ID:9322029 | HubMed [PSORTII]
  5. Horton et al., to be published. [WoLFPSORT]
  6. Bannai H, Tamada Y, Maruyama O, Nakai K, and Miyano S. Extensive feature detection of N-terminal protein sorting signals. Bioinformatics. 2002 Feb;18(2):298-305. PubMed ID:11847077 | HubMed [iPSORT]
All Medline abstracts: PubMed | HubMed
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