 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P50495 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MKIIFFLCSFLFFIINTQCVTHESYQELVKKLEALEDAVLTGYGLFHKEK 50
51 MILNEEEITTKGASAQSGTSGTSGTSGTSGTSGTSGTSAQSGTSGTSAQS 100
101 GTSGTSAQSGTSGTSGTSGTSPSSRSNTLPRSNTSSGASPPADASDSDAK 150
151 SYADLKHRVRNYLFTIKELKYPELFDLTNHMLTLCDNIHGFKYLIDGYEE 200
201 INELLYKLNFYFDLLRAKLNDVCANDYCQIPFNLKIRANELDVLKKLVFG 250
251 YRKPLDNIKDNVGKMEDYIKKNKTTIANINELIEGSKKTIDQNKNADNEE 300
301 GKKKLYQAQYDLSIYNKQLEEAHNLISVLEKRIDTLKKNENIKELLDKIN 350
351 EIKNPPPANSGNTPNTLLDKNKKIEEHEEKIKEIAKTIKFNIDSLFTDPL 400
401 ELEYYLREKNKKVDVTPKSQDPTKSVQIPKVPYPNGIVYPLPLTDIHNSL 450
451 AADNDKNSYGDLMNPDTKEKINEKIITDNKERKIFINNIKKQIDLEEKKI 500
501 NHTKEQNKKLLEDYEKSKKDYEELLEKFYEMKFNNNFDKDVVDKIFSARY 550
551 TYNVEKQRYNNKFSSSNNSVYNVQKLKKALSYLEDYSLRKGISEKDFNHY 600
601 YTLKTGLEADIKKLTEEIKSSENKILEKNFKGLTHSANASLEVYDIVKLQ 650
651 VQKVLLIKKIEDLRKIELFLKNAQLKDSIHVPNIYKPQNKPEPYYLIVLK 700
701 KEVDKLKEFIPKVKDMLKKEQAVLSSITQPLVAASETTEDGGHSTHTLSQ 750
751 SGETEVTEETEETEETVGHTTTVTITLPPKEVKVVENSIEHKSNDNSQAL 800
801 TKTVYLKKLDEFLTKSYICHKYILVSNSSMDQKLLEVYNLTPEEENELKS 850
851 CDPLDLLFNIQNNIPAMYSLYDSMNNDLQHLFFELYQKEMIYYLHKLKEE 900
901 NHIKKLLEEQKQITGTSSTSSPGNTTVNTAQSATHSNSQNQQSNASSTNT 950
951 QNGVAVSSGPAVVEESHDPLTVLSISNDLKGIVSLLNLGNKTKVPNPLTI 1000
1001 STTEMEKFYENILKNNDTYFNDDIKQFVKSNSKVITGLTETQKNALNDEI 1050
1051 KKLKDTLQLSFDLYNKYKLKLDRLFNKKKELGQDKMQIKKLTLLKEQLES 1100
1101 KLNSLNNPHNVLQNFSVFFNKKKEAEIAETENTLENTKILLKHYKGLVKY 1150
1151 YNGESSPLKTLSEVSIQTEDNYANLEKFRVLSKIDGKLNDNLHLGKKKLS 1200
1201 FLSSGLHQLITELKEVIKNKNYTGNSPSENNKKVNEALKSYENFLPEAKV 1250
1251 TTVVTPPQPDVTPSPLSVRVSGSSGSTKEETQIPTSGSLLTELQQVVQLQ 1300
1301 NYDEEDDSLVVLPIFGESEDNDEYLDQVVTGEAISVTMDNILSGFENEYD 1350
1351 VIYLKPLAGVYRSLKKQIEKNIFTFNLNLNDILNSRLKKRKYFLDVLESD 1400
1401 LMQFKHISSNEYIIEDSFKLLNSEQKNTLLKSYKYIKESVENDIKFAQEG 1450
1451 ISYYEKVLAKYKDDLESIKKVIKEEKEKFPSSPPTTPPSPAKTDEQKKES 1500
1501 KFLPFLTNIETLYNNLVNKIDDYLINLKAKINDCNVEKDEAHVKITKLSD 1550
1551 LKAIDDKIDLFKNHNDFDAIKKLINDDTKKDMLGKLLSTGLVQNFPNTII 1600
1601 SKLIEGKFQDMLNISQHQCVKKQCPENSGCFRHLDEREECKCLLNYKQEG 1650
1651 DKCVENPNPTCNENNGGCDADAKCTEEDSGSNGKKITCECTKPDSYPLFD 1700
1701 GIFCSSSNFLGISFLLILMLILYSFI 1726
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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