 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O14248 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MVQKVLSRQSDNSQDVSAEQLDVVESGSIDQQNIRAWVVRKVKENDKRTS 50
51 TNQSFKWEAVKPASCLDAANEKFMYLHGGREKSGISNSLFKLDLDSCTVY 100
101 SHNRGEDNDSPARVGHSIVCSADTIYLFGGCDSETDSTFEVGDNSLYAYN 150
151 FKSNQWNLVSTQSPLPSPRTGHSMLLVDSKLWIFGGECQGKYLNDIHLFD 200
201 TKGVDRRTQSELKQKANANNVEKANMEFDETDWSWETPFLHSSSPPPRSN 250
251 HSVTLVQGKIFVHGGHNDTGPLSDLWLFDLETLSWTEVRSIGRFPGPREG 300
301 HQATTIDDTVYIYGGRDNKGLILNELWAFNYSQQRWSLVSNPIPILLSDS 350
351 SSYKIVSKNNHILLLYLNALDAPKQLLCYEADPKNLYWDKDKFSDIPVLQ 400
401 HISMKPSNASNHTVSLGYLNDRPNHSKKNSVTSTSSSQFNNFLEQNQKAV 450
451 RSARHRHYASLDEQGLHSLRNLSKTSGMNHSADFSLHEFGQADPFAYEIE 500
501 KPIASLPLPNGNDTISRSSESSSPINESESNSLLKLQSDFKFSNSDDRVA 550
551 WLEEQLLYCMQQGYTLKPPNLFQHVDEKLRLEKKEQLSYLEILKVIEQML 600
601 ESNEQKFKKQIVSLASENAKLAAQRDAAVENANYSRSLIQKKTTDETVGS 650
651 LIEKVGKLEYEVQGTLEEATSYYQKNTELQQLLKQNESASELLKSRNEKL 700
701 CVDYDKLRSVFEEDSSKILSLQKENENLQSQILQISEELVDYRSRCEALE 750
751 YGNYELETKLIEMHDRVEMQTNVIEASASALDVSNTAILSFEDSLRRERD 800
801 EKSTLQQKCLNLQYEYENVRIELENLQSRALELESALEQSVSDAKYSKAI 850
851 MQSGLSKLLSSINENKDNLKEFSKSKQKISYLESQLEGLHELLRESQRLC 900
901 EGRTKELLNSQQKLYDLKHSYSSVMTEKSKLSDQVNDLTEQAKITQRKLS 950
951 EVQIALADSKMNQQLSGKDSTDVHLPTDFSASSSPLRSYFNEEDSFNNAS 1000
1001 AAHSSKESDIPSGGVFTKYRNHFGNLMTSEETKAPDNNDLHKRLSDVINS 1050
1051 QQKFLSLSPQVSKDYYDVRSKLNDTAGSFSGEEMRAIDDNYYASRIKQLE 1100
1101 DDYQKAITYANCSDESFQQLSHSFM 1125
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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