 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10289 from www.uniprot.org...
The NucPred score for your sequence is 0.38 (see score help below)
1 MRPEVEQELAHTLLLELLAYQFASPVRWIETQDVILSPPVSAERIVEIGP 50
51 SPTLAGMAKRTLKLKYENMDAALSINREVLCYSKDAREIYYNFEDEVADE 100
101 PAEAPASTSSTPKVETAAAAAPAATPAPAPAQTSAPAAALPDEPPKALEV 150
151 LHTLVAQKLKKSIEEVSPQKSIKDLVGGKSTLQNEILGDLQKEFGATPEK 200
201 PEEVPLDELGAIMQSSFNGSLGKQSSSLISRMISSKMPGGFNNSAVRGYL 250
251 GNRYGLGPGRLESVLLLALTMEPASRLGSEADAKAWLDSVAQKYAARNGV 300
301 TLSSPTAEGGSSSGSAAVIDEETFKKLTKNNTMLVTQQLELFARYLNKDL 350
351 RAGQKAQVAEKVISDTLRAQLDLWNEEHGEFYASGIAPIFSPLKARVYDS 400
401 DWNWARQDALKMFFDIIFGRLKHVDTEIVARCISVMNRSNPTLLEFMQYH 450
451 IDHCPAEKGETYQLAKTLGQQLIDNCKSVIDAPPVFKNVNHPTAPSTTID 500
501 ERGNLNYEEIPRPGVRKLTHYVTEMAKGGKLPTESKNKAKVQNDLARIYR 550
551 IIKSQNKMSRSSKLQIKQLYGQVLHALSLPLPSSNDEQTPVKETIPFLHI 600
601 RKKSVDGNWEFNKSLTGTYLDVLESGAKNGITYQDKYALVTGAGAGSIGA 650
651 QIVEGLLAGGAKVVVTTSRFSRKVTEFYQSLYTRHGSRGSCLIVVPFNQG 700
701 SKTDVEALIDYIYDEKKGLGWNLDYIVPFAAIPENGREIDGIDSRSEFAH 750
751 RIMLTNILRLLGAVKSQKASRGMDTRPAQVILPLSPNHGTFGNDGLYSES 800
801 KLGLETLFNRWYSESWANYLTICGAVIGWTRGTGLMAPNNIVSQGIEKYG 850
851 VRTFSQSEMAFNILGLMSQKVVDLCQSEPIYANLNGGLELLPDLKDLSTR 900
901 LRTELLETAEIRRAVAAETAFDHSITNGPDSEAVFQKTAIQPRANLKFNF 950
951 PKLKPYEALSHLSDLRGMVDLEKVPVVTGFSEVGPWGNSRTRWDMECYGE 1000
1001 FSLEGCVEIAWIMGLIKNFNGKGKDGKPYSGWVDTKTGEPVDDKDVKAKY 1050
1051 EKYILEHCGIRIIEAELFHGYNPEKKELLQEVVIDHDLEPFEASKEAAHE 1100
1101 FKLRHGDQVEIFEIPDSTEWSVRFKRGTSMLIPKALRFDRFVAGQIPLGW 1150
1151 DPKRYGIPDDIISQVDPTTLYVLVSTVEALVASGITDPYECYKYIHVSEL 1200
1201 GNTVGSGIGGMSALRGMYKDRWTDKPVQKDILQESFINTANAWINMLLLS 1250
1251 ASGPIKTPVGACATAVESVDAAVDLITSGKARICISGGYDDFSEEGSYEF 1300
1301 ANMGATSNAAKETERGRTPQEMSRPATSTRDGFMESQGAGVQIIMQAKLA 1350
1351 IEMGVPIHGIVGYVSTAMDKQGRSVPAPGQGILTGAREIATKTPLPIVDL 1400
1401 KFRSRQLQRRRSQIGEWAEREYLYLEEELDAMKVQNPDLDLEAYRIERIN 1450
1451 VIKEEVVRQEKEALNTFGNEFWKRDPTIAPIRGALAVWGLTIDDLGVASF 1500
1501 HGTSTKANEKNECDVIDSQLTHLGRSKGNAVYGVFQKYLTGHSKGGAGAW 1550
1551 MLNGALQILRSGFVPGNRNADNIDEYLARFDRVMFPSEGIQTDGIKAASV 1600
1601 TAFGFGQVGGQVIVIHPDYIYGVIDEATYNAYKAKTAARYKASYRYTHDA 1650
1651 LVYNNLVRAKDSPPYTKEQEKAVYLNPLARASKSKAGTWTFPATLPAESD 1700
1701 ISKTNETTRTLQSLTTSLTNSNENVGVDVELVSAISIDNETFIERNFTDT 1750
1751 ERKYCFAAPNPQASFAGRWSAKEAVFKSLGISGKGAAAPLKDIEIISSES 1800
1801 GAPEVVLHGEAAKAATTAGVKSVSVSISHDDNQSVSVALAHK 1842
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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