 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P28668 from www.uniprot.org...
The NucPred score for your sequence is 0.30 (see score help below)
1 MSIKLKANLNNPPISGLATAHLINGTVPVEIVWSKEETSLQFPDNRLLVC 50
51 HSNNDVLRALARAAPDYKLYGETAIERTQIDHWLSFSLTCEDDISWALSF 100
101 LDKSIAPVTYLVANKLTIADFALFNEMHSRYEFLAAKGIPQHVQRWYDLI 150
151 TAQPLIQKVLQSLPEDAKVKRSPQSSKEQTPAKTGERKQEGKFVDLPGAE 200
201 MGKVVVRFPPEASGYLHIGHAKAALLNQYYALAFQGTLIMRFDDTNPAKE 250
251 TVEFENVILGDLEQLQIKPDVFTHTSNYFDLMLDYCVRLIKESKAYVDDT 300
301 PPEQMKLEREQRVESANRSNSVEKNLSLWEEMVKGSEKGQKYCVRAKIDM 350
351 SSPNGCMRDPTIYRCKNEPHPRTGTKYKVYPTYDFACPIVDAIENVTHTL 400
401 RTTEYHDRDDQFYWFIDALKLRKPYIWSYSRLNMTNTVLSKRKLTWFVDS 450
451 GLVDGWDDPRFPTVRGIIRRGMTVEGLKEFIIAQGSSKSVVFMNWDKIWA 500
501 FNKKVIDPIAPRYTALEKEKRVIVNVAGAKVERIQVSVHPKDESLGKKTV 550
551 LLGPRIYIDYVDAEALKEGENATFINWGNILIRKVNKDASGNITSVDAAL 600
601 NLENKDFKKTLKLTWLAVEDDPSAYPPTFCVYFDNIISKAVLGKDEDFKQ 650
651 FIGHKTRDEVPMLGDPELKKCKKGDIIQLQRRGFFKVDVAYAPPSGYTNV 700
701 PSPIVLFSIPDGHTKDVPTSGLKVNAPDAKATKKASSPVSSSGQASELDS 750
751 QISQQGDLVRDLKSKKAAKDQIDVAVKKLLALKADYKSATGKDWKPGQTS 800
801 ASSAPVPAASSSSANDAVSVNASIVKQGDLVRDLKGKKASKPEIDAAVKT 850
851 LLELKAQYKTLTGQDWKPGTVPTTAAPSASAAPSVGVNDSVAQILSQITA 900
901 QGDKVRELKSAKADKATVDAAVKTLLSLKADYKAATGSDWKPGTTAPAPA 950
951 AAPVKVKQEKNPDPASVLTVNTLLNKIAQQGDKIRQLKSAKSEKSLVEAE 1000
1001 VKLLLALKTDYKSLTGQEWKPGTVAPAPTTVNVIDLTGGDSGSDVGSVLS 1050
1051 KIQAQGDKIRKLKSEKAAKNVIDPEVKTLLALKGEYKTLSGKDWTPDAKS 1100
1101 EPAVVKKEASPVSMASPAKDELTQEINAQGEKVRAAKGNKAAKEVIDAEV 1150
1151 AKLLALKAKYKEVTGTDFPVAGRGGGGGGGSAKKAPKEAQPKPAKPVKKE 1200
1201 PAADASGAVKKQTRLGLEATKEDNLPDWYSQVITKGEMIEYYDVSGCYIL 1250
1251 RQWSFAIWKAIKTWFDAEITRMGVKECYFPIFVSKAVLEKEKTHIADFAP 1300
1301 EVAWVTKSGDSDLAEPIAVRPTSETVMYPAYAKWVQSYRDLPIRLNQWNN 1350
1351 VVRWEFKQPTPFLRTREFLWQEGHTAFADKEEAAKEVLDILDLYALVYTH 1400
1401 LLAIPVVKGRKTEKEKFAGGDYTTTVEAFISASGRAIQGATSHHLGQNFS 1450
1451 KMFEIVYEDPETQQKKYVYQNSWGITTRTIGVMIMVHADNQGLVLPPHVA 1500
1501 CIQAIVVPCGITVNTKDDERAQLLDACKALEKRLVGGGVRCEGDYRDNYS 1550
1551 PGWKFNHWELKGVPLRLEVGPKDLKAQQLVAVRRDTVEKITIPLADVEKK 1600
1601 IPALLETIHESMLNKAQEDMTSHTKKVTNWTDFCGFLEQKNILLAPFCGE 1650
1651 ISCEDKIKADSARGEEAEPGAPAMGAKSLCIPFDQPAPIAASDKCINPSC 1700
1701 TNKPKFYTLFGRSY 1714
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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