Protein Localization

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Contents

LOChom

Database of subcellular localization predictions based on sequence homology to experimentally annotated proteins.

Subcellular localization is predicted using the explicit "Accuracy versus Scaled HSSP distance" curves for the five major localizations. For the minor(under represented) localization classes, the average(over all classes) "Accuracy versus Scaled HSSP distance curve" was used. The assigned localization is inferred from the homologue that most accurately predicts localization for the protein.

LOCkey

  1. predicted subcellular localization of eukaryotic proteins in SWISS-PROT.
  2. predict subcellular localization of your protein using LOCtree.
  3. SWISS-PROT keywords strongly correlated with subcellular localization.
  4. Follow this link for same list sorted by subcellular localization.
  5. Examples of Non-trivial predictions.
  6. Examples of incorrect predictions at high reliability.

LOCNet

Database of predicted subcellular localization for potential targets for structural genomics from TargetDb.

Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom ( using homology ), LOCkey (using keywords) and LOCtree (prediction based on hierarchical support vector machines). The reported localization is based on the method which predicts localization of a given protein with the highest confidence.

LOCTarget

Database of predicted subcellular localization for potential targets for structural genomics from TargetDb.

You submit a protein sequence or a potential NLS. PredictNLS predicts that your protein is nuclear or finds out whether your potential NLS is found in our database. The program also compiles statistics on the number of nuclear/non-nuclear proteins in which your potential NLS is found. Finally, proteins with similar NLS motifs are reported, and the experimental paper describing the particular NLS are given. If no NLS is found, you can predict the subcellular localization of the protein using LOCtree.

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