 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P07293 from www.uniprot.org...
The NucPred score for your sequence is 0.50 (see score help below)
1 MEPSSPQDEGLRKKQPKKPLPEVLPRPPRALFCLTLQNPLRKACISIVEW 50
51 KPFETIILLTIFANCVALAVYLPMPEDDNNSLNLGLEKLEYFFLTVFSIE 100
101 AAMKIIAYGFLFHQDAYLRSGWNVLDFIIVFLGVFTAILEQVNVIQSNTA 150
151 PMSSKGAGLDVKALRAFRVLRPLRLVSGVPSLQVVLNSIFKAMLPLFHIA 200
201 LLVLFMVIIYAIIGLELFKGKMHKTCYYIGTDIVATVENEKPSPCARTGS 250
251 GRPCTINGSECRGGWPGPNHGITHFDNFGFSMLTVYQCITMEGWTDVLYW 300
301 VNDAIGNEWPWIYFVTLILLGSFFILNLVLGVLSGEFTKEREKAKSRGTF 350
351 QKLREKQQLEEDLRGYMSWITQGEVMDVEDLREGKLSLEEGGSDTESLYE 400
401 IEGLNKIIQFIRHWRQWNRVFRWKCHDLVKSRVFYWLVILIVALNTLSIA 450
451 SEHHNQPLWLTHLQDIANRVLLSLFTIEMLLKMYGLGLRQYFMSIFNRFD 500
501 CFVVCSGILELLLVESGAMTPLGISVLRCIRLLRLFKITKYWTSLSNLVA 550
551 SLLNSIRSIASLLLLLFLFIIIFALLGMQLFGGRYDFEDTEVRRSNFDNF 600
601 PQALISVFQVLTGEDWNSVMYNGIMAYGGPSYPGVLVCIYFIILFVCGNY 650
651 ILLNVFLAIAVDNLAEAESLTSAQKAKAEERKRRKMSRGLPDKTEEEKSV 700
701 MAKKLEQKPKGEGIPTTAKLKVDEFESNVNEVKDPYPSADFPGDDEEDEP 750
751 EIPVSPRPRPLAELQLKEKAVPIPEASSFFIFSPTNKVRVLCHRIVNATW 800
801 FTNFILLFILLSSAALAAEDPIRAESVRNQILGYFDIAFTSVFTVEIVLK 850
851 MTTYGAFLHKGSFCRNYFNILDLLVVAVSLISMGLESSTISVVKILRVLR 900
901 VLRPLRAINRAKGLKHVVQCVFVAIRTIGNIVLVTTLLQFMFACIGVQLF 950
951 KGKFFSCNDLSKMTEEECRGYYYVYKDGDPTQMELRPRQWIHNDFHFDNV 1000
1001 LSAMMSLFTVSTFEGWPQLLYRAIDSNEEDMGPVYNNRVEMAIFFIIYII 1050
1051 LIAFFMMNIFVGFVIVTFQEQGETEYKNCELDKNQRQCVQYALKARPLRC 1100
1101 YIPKNPYQYQVWYVVTSSYFEYLMFALIMLNTICLGMQHYHQSEEMNHIS 1150
1151 DILNVAFTIIFTLEMILKLLAFKARGYFGDPWNVFDFLIVIGSIIDVILS 1200
1201 EIDTFLASSGGLYCLGGGCGNVDPDESARISSAFFRLFRVMRLIKLLSRA 1250
1251 EGVRTLLWTFIKSFQALPYVALLIVMLFFIYAVIGMQMFGKIALVDGTQI 1300
1301 NRNNNFQTFPQAVLLLFRCATGEAWQEILLACSYGKLCDPESDYAPGEEY 1350
1351 TCGTNFAYYYFISFYMLCAFLIINLFVAVIMDNFDYLTRDWSILGPHHLD 1400
1401 EFKAIWAEYDPEAKGRIKHLDVVTLLRRIQPPLGFGKFCPHRVACKRLVG 1450
1451 MNMPLNSDGTVTFNATLFALVRTALKIKTEGNFEQANEELRAIIKKIWKR 1500
1501 TSMKLLDQVIPPIGDDEVTVGKFYATFLIQEHFRKFMKRQEEYYGYRPKK 1550
1551 DTVQIQAGLRTIEEEAAPEIRRTISGDLTAEEELERAMVEAAMEERIFRR 1600
1601 TGGLFGQVDTFLERTNSLPPVMANQRPLQFAEIEMEELESPVFLEDFPQD 1650
1651 ARTNPLARANTNNANANVAYGNSNHSNNQMFSSVHCEREFPGEAETPAAG 1700
1701 RGALSHSHRALGPHSKPCAGKLNGQLVQPGMPINQAPPAPCQQPSTDPPE 1750
1751 RGQRRTSLTGSLQDEAPQRRSSEGSTPRRPAPATALLIQEALVRGGLDTL 1800
1801 AADAGFVTATSQALADACQMEPEEVEVAATELLKARESVQGMASVPGSLS 1850
1851 RRSSLGSLDQVQGSQETLIPPRP 1873
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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