 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O54952 from www.uniprot.org...
The NucPred score for your sequence is 1.00 (see score help below)
1 MDLSAVRIQEVQNVLHAMQKILECPICLELIKEPVSTQCDHIFCKFCMLK 50
51 LLNQKKGPSQCPLCKNEITKRSLQGSARFSQLVEELLKIIDAFELDTGMQ 100
101 CANGFSFSKKKNSSSELLNEDASIIQSVGYRNRVKKLQQIESGSATLKDS 150
151 LSVQLSNLGIVRSMKKNRQTQPQNKSVYIALESDSSEERVNAPDGCSVRD 200
201 QELFQIAPGGAGDEGKLNSAKKAACDFSEGIRNIEHHQCSDKDLNPTENH 250
251 ATERHPEKCPRISVANVHVEPCGTDARASSLQRGTRSLLFTEDRLDAEKA 300
301 EFCDRSKQSGAAVSQQSRWADSKETCNGRPVPRTEGKADPNVDSLCGRKQ 350
351 WNHPKSLCPENSGATTDVPWITLNSSIQKVNEWFSRTGEMLTSDNASDRR 400
401 PASNAEAAVVLEVSNEVDGCFSSSKKIDLVAPDPDNAVMCTSGRDFSKPV 450
451 ENIINDKIFGKTYQRKGSRPHLNHVTEIIGTFTTEPQIIQEQPFTNKLKR 500
501 KRSTCLHPEDFIKKADLTVVQRISENLNQGTDQMEPNDQAMSITSNGQEN 550
551 RATGNDLQRGRNAHPIESLRKEPAFTAKAKSISNSISDLEVELNVHSSKA 600
601 PKKNRLRRKSTRCVLPLEPISRNPSPPTCAELQIESCGSSEETKKNNSNQ 650
651 TPAGHIREPQLIEDTEPAADAKKNEPNEHIRKRSASDAFPEEKLMNKAGL 700
701 LTSCSSPRKPQGPVNPSPERKGIEQLEMCQMPDNNKELGDLVLGGEPSGK 750
751 PTEPSEESTSVSLVPDTDYDTQNSVSILEANTVRYARTGSVQCMTQFVAS 800
801 ENPKELVHGSNNAGSGSECFKHPLRHELNHNQETIEMEDSELDTQYLQNT 850
851 FQVSKRQSFALFSKLRSPQKDCTLVGARSVPSREPSPKVTSRGEQKERQG 900
901 QEESEISHVQAVTVTVGLPVPCQEGKPGAVTMCADVSRLCPSSHYRSCEN 950
951 GLNTTDKSGISQNSHFRQSVSPLRSSIKTDNRKTLTEGRFEKHTERGMGN 1000
1001 ETAVQSTIHTISLNNRGDACLEASSGSVIEVHSTGENVQGQLDRNRGPKV 1050
1051 NTVSLLDSTQPGVSKQSAPVSDKYLEIKQESKAVSADFSPCLFSDHLEKP 1100
1101 MRSDKTFQVCSETPDDLLDDVEIQENASFGEGGITEKSAIFNGSVLRRES 1150
1151 SRSPSPVTHASKSRSLHRGSRKLEFSEESDSTEDEDLPCFQHLLSRVSST 1200
1201 PELTRCSSVVTQRVPEKAKGTQAPRKSSISDCNNEVILGEASQEYQFSED 1250
1251 AKCSGSMFSSQHSAALGSPANALSQDPDFNPPSKQRRHQAENEEAFLSDK 1300
1301 ELISDHEDMAACLEEASDQEEDSIIPDSVASGYESEANLSEDCSQSDILT 1350
1351 TQQRATMKDNLIKLQQEMAQLEAVLEQHGSQPSGHPPCLPADPCALEDLP 1400
1401 DPEQNRSGTAILTSKNINENPVSQNPKRACDDKSQPQPPDGLPSGDKESG 1450
1451 MRRPSPFKSPLTSSRCSARGHSRSLQNRNSTSQEELLQPAXLEKSCEPHN 1500
1501 LTGRSCLPRQDLEGTPYPESGIRLVSSRDPDSESPKVSALVCTAPASTSA 1550
1551 LKISQGQVAGSCRSPAAGGADTAVVEIVSKIKPEVTSPKERAERDISMVV 1600
1601 SGLTPKEVMIVQKFAEKYRLALTDVITEETTHVIIKTDAEFVCERTLKYF 1650
1651 LGIAGGKWIVSYSWVIKSIQERKLLSVHEFEVKGDVVTGSNHQGPRRSRE 1700
1701 SQEKLFEGLQIYCCEPFTNMPKDELERMLQLCGASVVKELPLLTRDTGAH 1750
1751 PIVLVQPSAWTEDNDCPDIGQLCKGRLVMWDWVLDSISVYRCRDLDAYLV 1800
1801 QNITCGRDGSEPQDSND 1817
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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