 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9SMV7 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MQRQRSILSFFQKPTAATTKGLVSGDAASGGGGSGGPRFNVKEGDAKGDA 50
51 SVRFAVSKSVDEVRGTDTPPEKVPRRVLPSGFKPAESAGDASSLFSNIMH 100
101 KFVKVDDRDCSGERSREDVVPLNDSSLCMKANDVIPQFRSNNGKTQERNH 150
151 AFSFSGRAELRSVEDIGVDGDVPGPETPGMRPRASRLKRVLEDEMTFKED 200
201 KVPVLDSNKRLKMLQDPVCGEKKEVNEGTKFEWLESSRIRDANRRRPDDP 250
251 LYDRKTLHIPPDVFKKMSASQKQYWSVKSEYMDIVLFFKVGKFYELYELD 300
301 AELGHKELDWKMTMSGVGKCRQVGISESGIDEAVQKLLARGYKVGRIEQL 350
351 ETSDQAKARGANTIIPRKLVQVLTPSTASEGNIGPDAVHLLAIKEIKMEL 400
401 QKCSTVYGFAFVDCAALRFWVGSISDDASCAALGALLMQVSPKEVLYDSK 450
451 GLSREAQKALRKYTLTGSTAVQLAPVPQVMGDTDAAGVRNIIESNGYFKG 500
501 SSESWNCAVDGLNECDVALSALGELINHLSRLKLEDVLKHGDIFPYQVYR 550
551 GCLRIDGQTMVNLEIFNNSCDGGPSGTLYKYLDNCVSPTGKRLLRNWICH 600
601 PLKDVESINKRLDVVEEFTANSESMQITGQYLHKLPDLERLLGRIKSSVR 650
651 SSASVLPALLGKKVLKQRVKAFGQIVKGFRSGIDLLLALQKESNMMSLLY 700
701 KLCKLPILVGKSGLELFLSQFEAAIDSDFPNYQNQDVTDENAETLTILIE 750
751 LFIERATQWSEVIHTISCLDVLRSFAIAASLSAGSMARPVIFPESEATDQ 800
801 NQKTKGPILKIQGLWHPFAVAADGQLPVPNDILLGEARRSSGSIHPRSLL 850
851 LTGPNMGGKSTLLRATCLAVIFAQLGCYVPCESCEISLVDTIFTRLGASD 900
901 RIMTGESTFLVECTETASVLQNATQDSLVILDELGRGTSTFDGYAIAYSV 950
951 FRHLVEKVQCRMLFATHYHPLTKEFASHPRVTSKHMACAFKSRSDYQPRG 1000
1001 CDQDLVFLYRLTEGACPESYGLQVALMAGIPNQVVETASGAAQAMKRSIG 1050
1051 ENFKSSELRSEFSSLHEDWLKSLVGISRVAHNNAPIGEDDYDTLFCLWHE 1100
1101 IKSSYCVPK 1109
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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