 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O15015 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MEDTPPSLSCSDCQRHFPSLPELSRHRELLHPSPNQDSEEADSIPRPYRC 50
51 QQCGRGYRHPGSLVNHRRTHETGLFPCTTCGKDFSNPMALKSHMRTHAPE 100
101 GRRRHRPPRPKEATPHLQGETVSTDSWGQRLGSSEGWENQTKHTEETPDC 150
151 ESVPDPRAASGTWEDLPTRQREGLASHPGPEDGADGWGPSTNSARAPPLP 200
201 IPASSLLSNLEQYLAESVVNFTGGQEPTQSPPAEEERRYKCSQCGKTYKH 250
251 AGSLTNHRQSHTLGIYPCAICFKEFSNLMALKNHSRLHAQYRPYHCPHCP 300
301 RVFRLPRELLEHQQSHEGERQEPRWEEKGMPTTNGHTDESSQDQLPSAQM 350
351 LNGSAELSTSGELEDSGLEEYRPFRCGDCGRTYRHAGSLINHRKSHQTGV 400
401 YPCSLCSKQLFNAAALKNHVRAHHRPRQGVGENGQPSVPPAPLLLAETTH 450
451 KEEEDPTTTLDHRPYKCSECGRAYRHRGSLVNHRHSHRTGEYQCSLCPRK 500
501 YPNLMALRNHVRVHCKAARRSADIGAEGAPSHLKVELPPDPVEAEAAPHT 550
551 DQDHVCKHEEEATDITPAADKTAAHICSICGLLFEDAESLERHGLTHGAG 600
601 EKENSRTETTMSPPRAFACRDCGKSYRHSGSLINHRQTHQTGDFSCGACA 650
651 KHFHTMAAMKNHLRRHSRRRSRRHRKRAGGASGGREAKLLAAESWTRELE 700
701 DNEGLESPQDPSGESPHGAEGNLESDGDCLQAESEGDKCGLERDETHFQG 750
751 DKESGGTGEGLERKDASLLDNLDIPGEEGGGTHFCDSLTGVDEDQKPATG 800
801 QPNSSSHSANAVTGWQAGAAHTCSDCGHSFPHATGLLSHRPCHPPGIYQC 850
851 SLCPKEFDSLPALRSHFQNHRPGEATSAQPFLCCLCGMIFPGRAGYRLHR 900
901 RQAHSSSGMTEGSEEEGEEEGVAEAAPARSPPLQLSEAELLNQLQREVEA 950
951 LDSAGYGHICGCCGQTYDDLGSLERHHQSQSSGTTADKAPSPLGVAGDAM 1000
1001 EMVVDSVLEDIVNSVSGEGGDAKSQEGAGTPLGDSLCIQGGESLLEAQPR 1050
1051 PFRCNQCGKTYRHGGSLVNHRKIHQTGDFLCPVCSRCYPNLAAYRNHLRN 1100
1101 HPRCKGSEPQVGPIPEAAGSSELQVGPIPEGGSNKPQHMAEEGPGQAEVE 1150
1151 KLQEELKVEPLEEVARVKEEVWEETTVKGEEIEPRLETAEKGCQTEASSE 1200
1201 RPFSCEVCGRSYKHAGSLINHRQSHQTGHFGCQACSKGFSNLMSLKNHRR 1250
1251 IHADPRRFRCSECGKAFRLRKQLASHQRVHMERRGGGGTRKATREDRPFR 1300
1301 CGQCGRTYRHAGSLLNHRRSHETGQYSCPTCPKTYSNRMALKDHQRLHSE 1350
1351 NRRRRAGRSRRTAVRCALCGRSFPGRGSLERHLREHEETEREPANGQGGL 1400
1401 DGTAASEANLTGSQGLETQLGGAEPVPHLEDGVPRPGERSQSPIRAASSE 1450
1451 APEPLSWGAGKAGGWPVGGGLGNHSGGWVPQFLTRSEEPEDSVHRSPCHA 1500
1501 GDCQLNGPTLSHMDSWDNRDNSSQLQPGSHSSCSQCGKTYCQSGSLLNHN 1550
1551 TNKTDRHYCLLCSKEFLNPVATKSHSHNHIDAQTFACPDCGKAFESHQEL 1600
1601 ASHLQAHARGHSQVPAQMEEARDPKAGTGEDQVVLPGQGKAQEAPSETPR 1650
1651 GPGESVERARGGQAVTSMAAEDKERPFRCTQCGRSYRHAGSLLNHQKAHT 1700
1701 TGLYPCSLCPKLLPNLLSLKNHSRTHTDPKRHCCSICGKAFRTAARLEGH 1750
1751 GRVHAPREGPFTCPHCPRHFRRRISFVQHQQQHQEEWTVAGSGAPVAPVT 1800
1801 GRGDLPLPPPPTPTTPLLDPSPQWPADLSFSL 1832
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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