 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q12649 from www.uniprot.org...
The NucPred score for your sequence is 0.10 (see score help below)
1 MSLPNHSGSSAFKSFSYIVGTGIKRAAKLSTRNPIEMIVVVLILSSFSYF 50
51 YLFNLARTSDIFSGTVTRLYPTSVYAPTKDHSFSVVDRTADSTIANNAVK 100
101 VHLHQIVVSDPKHGVLSRQTLASVLRFQQMAENEIYVPDSTAVNRFAFNK 150
151 DLCYKTTLPSSYSSHSSDSNSNSNLNSKTSPCFAHSPADIWQDEATLLAD 200
201 KNIRSTIEANLDTAKNVFGDLQLNATYASSVTLSYAFNTTGDYREHLADM 250
251 WKHKVATLPPADLVSLSNIGQQENVFAWLFIVTRNVIFRVKELIDLADNI 300
301 DIIVILVGYIMMIATFISLYVNMRAMGSRYTLATAVVFNGFFSFMLALLT 350
351 VRALGVDVYPVVLAEAIPFLAVTIGFERPFKLTKRVFQFSKETPLTKQEI 400
401 RTTIMRAVDTVALPIARDCFMEIIVLVLGAKSGISGLEEFCLLSAILLAY 450
451 DFIIMFTWYTAVLALKLELLRIREINGISADDIKKGTKKSTGYIRRTVIK 500
501 AFSDDHAAGANTANQKADGPIIGRVKLLMIVGFVVMHIFKFCSAFQSVGP 550
551 QVNITEPSIAVVLDQLLEQHKASSQASLPLFVQVFPAMPFHVATVNKSFV 600
601 PDAITRPLEALFDTYAVYIQHPVISKWLTIALFVSLFLNTYLFNVAKQPK 650
651 QIVEQVNQDKKITNAIESTNNTHIEVTEKQKPTIQSPGPVVSSAVVMSPN 700
701 HKRSHNHHHSHSHSHNHHSNHHQSDIVRPIDECVALVRTPEMLNDEEVIS 750
751 LVENGKMASYALEKVLGDLQRAVGIRRALISRASITKTLEASALPLENYH 800
801 YDKVMGACCENVIGYMPIPVGVAGPMNIDGDLIHIPMATTEGCLVASTAR 850
851 GCKAINAGGGASTIVIADGMTRGPCVEFPTILRAAACKLWIENEGNDIVT 900
901 NAFNSTSRFARLRKLKIALAGKLVFIRFSTTTGDAMGMNMISKGCEKALS 950
951 IITEHFPDMQIISLSGNYCTDKKPAAINWIEGRGKSVVTEAVIPGAIVEK 1000
1001 VLKTTVAALVELNISKNLIGSAMAGSVGGFNAHAANILTAIYLATGQDPA 1050
1051 QNVESSNCITLMKAVNDTKDLHISCTMPSIEVGTIGGGTILPPQQSMLDM 1100
1101 LGVRGPHPTEPGKNAQRLARIICAAVMAGELSLCAALAAGHLVKAHMAHN 1150
1151 RGTQAPTITSGPAPSTGTEPGTCIKS 1176
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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