 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q06190 from www.uniprot.org...
The NucPred score for your sequence is 0.75 (see score help below)
1 MAATYRLVVSTVNHYSSVVIDRRFEQAIHYCTGTCHTFTHGIDCIVVHHS 50
51 VCADLLHIPVSQFKDADLNSMFLPHENGLSSAEGDYPQQAFTGIPRVKRG 100
101 STFQNTYNLKDIAGEAISFASGKIKEFSFEKLKNSNHAAYRKGRKVKSDS 150
151 FNRRSVDLDLLCGHYNNDGNAPSFGLLRSSSVEEKPLSHRNSLDTNLTSM 200
201 FLQNFSEEDLVTQILEKHKIDNFSSGTDIKMCLDILLKCSEDLKKCTDII 250
251 KQCIKKKSGSSISEGSGNDTISSSETVYMNVMTRLASYLKKLPFEFMQSG 300
301 NNEALDLTELISNMPSLQLTPFSPVFGTEQPPKYEDVVQLSASDSGRFQT 350
351 IELQNDKPNSRKMDTVQSIPNNSTNSLYNLEVNDPRTLKAVQVQSQSLTM 400
401 NPLENVSSDDLMETLYIEEESDGKKALDKGQKTENGPSHELLKVNEHRAE 450
451 FPEHATHLKKCPTPMQNEIGKIFEKSFVNLPKEDCKSKVSKFEEGDQRDF 500
501 TNSSSQEEIDKLLMDLESFSQKMETSLREPLAKGKNSNFLNSHSQLTGQT 550
551 LVDLEPKSKVSSPIEKVSPSCLTRIIETNGHKIEEEDRALLLRILESIED 600
601 FAQELVECKSSRGSLSQEKEMMQILQETLTTSSQANLSVCRSPVGDKAKD 650
651 TTSAVLIQQTPEVIKIQNKPEKKPGTPLPPPATSPSSPRPLSPVPHVNNV 700
701 VNAPLSINIPRFYFPEGLPDTCSNHEQTLSRIETAFMDIEEQKADIYEMG 750
751 KIAKVCGCPLYWKAPMFRAAGGEKTGFVTAQSFIAMWRKLLNNHHDDASK 800
801 FICLLAKPNCSSLEQEDFIPLLQDVVDTHPGLTFLKDAPEFHSRYITTVI 850
851 QRIFYTVNRSWSGKITSTEIRKSNFLQTLALLEEEEDINQITDYFSYEHF 900
901 YVIYCKFWELDTDHDLYISQADLSRYNDQASSSRIIERIFSGAVTRGKTI 950
951 QKEGRMSYADFVWFLISEEDKRNPTSIEYWFRCMDVDGDGVLSMYELEYF 1000
1001 YEEQCERMEAMGIEPLPFHDLLCQMLDLVKPAVDGKITLRDLKRCRMAHI 1050
1051 FYDTFFNLEKYLDHEQRDPFAVQKDVENDGPEPSDWDRFAAEEYETLVAE 1100
1101 ESAQAQFQEGFEDYETDEPASPSEFGNKSNKILSASLPEKCGKLQSVDEE 1150
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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