 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P40456 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MVDLMVPANDDPSNETDYSRSNNNHTHIVSDMRPTSAAFLHQKRHSSSSH 50
51 NDTPESSFAKRRVPGIVDPVGKGFIDGITNSQISAQNTPSKTDDASRRPS 100
101 ISRKVMESTPQVKTSSIPTMDVPKSPYYVNRTMLARNMKVVSRDTYEDNA 150
151 NPQMRADEPLVASNGIYSNSQPQSQVTLSDIRRAPVVAASPPPMIRQLPS 200
201 AQPNQTFIKKLQEIYKIIVVQETELQQRCLYLTTSQTTELKSLWAIYRLN 250
251 TELIKNYINFIITALLTTQPINDLIMGQEILDIYRIEKRLWVYGIITFLD 300
301 VLKNFSNFMDPEVCCQFIIYAFISVSNMLEDIPLKYSILWRQRLGDLSRM 350
351 AISLYPSGFIDWRLSAEYWYTESMKYIYGCGKLYYHIATVQQNSLEAFVN 400
401 LGKSVFCQDPFTPSQQTLQLLIENIYQSAFIDRSSGSANNNEIAHRNSQL 450
451 IDYLKHTEVMLLPSFLENMDLQHVVLMYFKDKFGKDFNGNDVFDTKDMFC 500
501 QNPESLRYYFRHAPAFAESQLLQLIGFGNPKNPFALLFQLPKYLKLKKDK 550
551 REKKRSEATETSSYTDPFDVQISSESYFQNIDALNSSFNDIPTNLNIWLD 600
601 SLNHINMTSIQCSIHVLTKFLHAPLVVALPHFLTWLHFIVAILKKLEMVN 650
651 SKQVVAFWIHFLRRTMPWNSIVTLGNVLVCYMLDNLHPFLKKELEKFYSL 700
701 ELDDLIEYYNENENLPEIWKCWGTLWFDAIKKCDVMEIPGVQDHLFFDSP 750
751 LDGIVFDEKDEVGEKFWMRSVRAVLLLKGIAKKFPDLGLKVSFQASVFCR 800
801 RNDIPPDYFLKNLTFKLDAYDEDNYNDNNELDDLYDTIEINEEIEAVNMD 850
851 PQATPNLSVVSGESIFEYTGYTRLAPDYHCFDKNGGFNSAFIYSQWSNVG 900
901 NGVTLDVSGESIYDVANNNLSLHWEKIFFDKIAAASKGSDENYNCTLYFV 950
951 IDATSWLRHFAHIFKLAKNNTLKFAICLTTFQELRYLRGSKDDTVVEAAT 1000
1001 RSVITIRQLYDEKKIIPMRFTGNIATHVEENLEFEEQITWKTHVDEFVID 1050
1051 AIAKLNQRFQAERLTDENKNKGKEFAVLVTDDDNMNQKAKDRMIKTCNTK 1100
1101 YLFSLGSKLGINSGLCTN 1118
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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