 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P32333 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MTSRVSRLDRQVILIETGSTQVVRNMAADQMGDLAKQHPEDILSLLSRVY 50
51 PFLLVKKWETRVTAARAVGGIVAHAPSWDPNESDLVGGTNEGSPLDNAQV 100
101 KLEHEMKIKLEEATQNNQLNLLQEDHHLSSLSDWKLNEILKSGKVLLASS 150
151 MNDYNVLGKADDNIRKQAKTDDIKQETSMLNASDKANENKSNANKKSARM 200
201 LAMARRKKKMSAKNTPKHPVDITESSVSKTLLNGKNMTNSAASLATSPTS 250
251 NQLNPKLEITEQADESKLMIESTVRPLLEQHEIVAGLVWQFQGIYELLLD 300
301 NLMSENWEIRHGAALGLRELVKKHAYGVSRVKGNTREENNLRNSRSLEDL 350
351 ASRLLTVFALDRFGDYVYDTVVAPVRESVAQTLAALLIHLDSTLSIKIFN 400
401 CLEQLVLQDPLQTGLPNKIWEATHGGLLGIRYFVSIKTNFLFAHGLLENV 450
451 VRIVLYGLNQSDDDVQSVAASILTPITSEFVKLNNSTIEILVTTIWSLLA 500
501 RLDDDISSSVGSIMDLLAKLCDHQEVLDILKNKALEHPSEWSFKSLVPKL 550
551 YPFLRHSISSVRRAVLNLLIAFLSIKDDSTKNWLNGKVFRLVFQNILLEQ 600
601 NPELLQLSFDVYVALLEHYKVKHTEKTLDHVFSKHLQPILHLLNTPVGEK 650
651 GKNYAMESQYILKPSQHYQLHPEKKRSISETTTDSDIPIPKNNEHINIDA 700
701 PMIAGDITLLGLDVILNTRIMGAKAFALTLSMFQDSTLQSFFTNVLVRCL 750
751 ELPFSTPRMLAGIIVSQFCSSWLQKHPEGEKLPSFVSEIFSPVMNKQLLN 800
801 RDEFPVFRELVPSLKALRTQCQSLLATFVDVGMLPQYKLPNVAIVVQGET 850
851 EAGPHAFGVETAEKVYGEYYDKMFKSMNNSYKLLAKKPLEDSKHRVLMAI 900
901 NSAKESAKLRTGSILANYASSILLFDGLPLKLNPIIRSLMDSVKEERNEK 950
951 LQTMAGESVVHLIQQLLENNKVNVSGKIVKNLCGFLCVDTSEVPDFSVNA 1000
1001 EYKEKILTLIKESNSIAAQDDINLAKMSEEAQLKRKGGLITLKILFEVLG 1050
1051 PSILQKLPQLRSILFDSLSDHENEEASKVDNEQGQKIVDSFGVLRALFPF 1100
1101 MSDSLRSSEVFTRFPVLLTFLRSNLSVFRYSAARTFADLAKISSVEVMAY 1150
1151 TIREILPLMNSAGSLSDRQGSTELIYHLSLSMETDVLPYVIFLIVPLLGR 1200
1201 MSDSNEDVRNLATTTFASIIKLVPLEAGIADPKGLPEELVASRERERDFI 1250
1251 QQMMDPSKAKPFKLPIAIKATLRKYQQDGVNWLAFLNKYHLHGILCDDMG 1300
1301 LGKTLQTICIIASDQYLRKEDYEKTRSVESRALPSLIICPPSLTGHWENE 1350
1351 FDQYAPFLKVVVYAGGPTVRLTLRPQLSDADIIVTSYDVARNDLAVLNKT 1400
1401 EYNYCVLDEGHIIKNSQSKLAKAVKEITANHRLILTGTPIQNNVLELWSL 1450
1451 FDFLMPGFLGTEKMFQERFAKPIAASRNSKTSSKEQEAGVLALEALHKQV 1500
1501 LPFMLRRLKEDVLSDLPPKIIQDYYCELGDLQKQLYMDFTKKQKNVVEKD 1550
1551 IENSEIADGKQHIFQALQYMRKLCNHPALVLSPNHPQLAQVQDYLKQTGL 1600
1601 DLHDIINAPKLSALRTLLFECGIGEEDIDKKASQDQNFPIQNVISQHRAL 1650
1651 IFCQLKDMLDMVENDLFKKYMPSVTYMRLDGSIDPRDRQKVVRKFNEDPS 1700
1701 IDCLLLTTKVGGLGLNLTGADTVIFVEHDWNPMNDLQAMDRAHRIGQKKV 1750
1751 VNVYRIITKGTLEEKIMGLQKFKMNIASTVVNQQNSGLASMDTHQLLDLF 1800
1801 DPDNVTSQDNEEKNNGDSQAAKGMEDIANETGLTGKAKEALGELKELWDP 1850
1851 SQYEEEYNLDTFIKTLR 1867
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.) |
Go back to the NucPred Home Page.