 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P25167 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MVELKMGDHNVEATTWDPGDSKDWSVPIKPLTEKWKLVPAFLQVKGLVKQ 50
51 HIDSFNHFINVDIKKIVKANELVTSGADPLFYLKYLDVRVGKPDIDDGFN 100
101 ITKATTPHECRLRDTTYSAPITVDIEYTRGTQRIKRNNLLIGRMPLMLRC 150
151 SNCALTGKSEFELSKLNECPLDPGGYFVVRGQEKVILIQEQLSWNKMLTE 200
201 DFNGVVQCQVTSSTHEKKSRTLVLSKHGKYYLKHNSMTDDIPIVVIFKAL 250
251 GVVSDQEIQSLIGIDSKSQNRFGASLIDAYNLKVFTQQRALEYMGSKLVV 300
301 KRFQSATTKTPSEEARELLLTTILAHVPVDNFNLQMKAIYVSMMVRRVMA 350
351 AELDKTLFDDRDYYGNKRLELAGSLLSMMFEDLFKRMNWELKTIADKNIP 400
401 KVKAAQFDVVKHMRAAQITAGLESAISSGNWTIKRFKMERAGVTQVLSRL 450
451 SYISALGMMTRVNSQFEKTRKVSGPRSLQPSQWGMLCPSDTPEGEACGLV 500
501 KNLALMTHITTEVEERPVMIVAFNAGVEDIREVSGNPINNPNVFLVFING 550
551 NVLGLTLNHKHLVRNLRYMRRKGRMGSYVSVHTSYTQRCIYIHTDGGRLC 600
601 RPYVIVENRRPLVKQHHLDELNRGIRKFDDFLLDGLIEYLDVNEENDSFI 650
651 AWNEDQIEDRTTHLEIEPFTLLGVCAGLVPYPHHNQSPRNTYQCAMGKQA 700
701 MGMIGYNQKNRIDSLMYNLVYPHAPMVKSKTIELTNFDKLPAGQNATVAV 750
751 MSYSGYDIEDALILNKASIDRGYGRCLVYKNSKCTVKRYANQTFDRIMGP 800
801 MKDALTNKVIFKHDVLDTDGIVAPGEQVQNKQIMINKEMPAVTSMNPLQG 850
851 QSAQVPYTAVPISYKGPEPSYIERVMVSANAEEDFLIKILLRQTRIPEIG 900
901 DKFSSRHGQKGVTGLIVEQEDMPFNDFGICPDMIMNPHGFPSRMTVGKTL 950
951 ELLGGKAGLLEGKFHYGTAFGGSKVEDIQAELERHGFNYVGKDFFYSGIT 1000
1001 GTPLEAYIYSGPVYYQKLKHMVQDKMHARARGPKAVLTRQPTQGRSREGG 1050
1051 LRLGEMERDCLISYGASMLIMERLMISSDAFEVDVCRTCGRMAYCSWCHF 1100
1101 CQSSANVSKISMPYACKLLFQELTSMNVVPKMILENY 1137
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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