 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q28204 from www.uniprot.org...
The NucPred score for your sequence is 0.53 (see score help below)
1 MANGGGGGGGGGGGSSLRMSSNIHANHLSLDASSSSSSSSSSSSSSSSVH 50
51 EPKMDALIIPVTMEVPCDSRGQRMWWAFLASSMVTFFGGLFIILLWRTLK 100
101 YLWTVCCHCGGKTKEAQKINNGSSQADGTLKPVDEKEETVAAEVGWMTSV 150
151 KDWAGVMISAQTLTGRVLVVLVFALSIGALVIYFIDSSNPIESCQNFYKD 200
201 FTLQIDMAFNVFFLLYFGLRFIAANDKLWFWLEVNSVVDFFTVPPVFVSV 250
251 YLNRSWLGLRFLRALRLIQFSEILQFLNILKTSNSIKLVNLLSIFISTWL 300
301 TAAGFIHLVENSGDPWENFQNNQALTYWECVYLLMVTMSTVGYGDVYAKT 350
351 TLGRLFMVFFILGGLAMFASYVPEIIELIGNRKKYGGSYSAVSGRKHIVV 400
401 CGHITLESVSNFLKDFLHKDRDDVNVEIVFLHNISPNLELEALFKRHFTQ 450
451 VEFYQGSVLNPHDLARVKIESADACLILANKYCADPDAEDASNIMRVISI 500
501 KNYHPKIRIITQMLQYHNKAHLLNIPSWNWKEGDDAICLAELKLGFIAQS 550
551 CLAQGLSTMLANLFSMRSFIKIEEDTWQKYYLEGVSNEMYTEYLSSAFVG 600
601 LSFPTVCELCFVKLKLLMIAIEYKSANRESRILINPGNHLKIQEGTLGFF 650
651 IASDAKEVKRAFFYCKACHDDITDPKRIKKCGCKRLEDEQPSTLSPKKKQ 700
701 RNGGMRNSPSSSPKLMRHDPLLIPGNDQIDNMDSNVKKYDSTGMFHWCAP 750
751 KEIEKVILTRSEAAMTVLSGHVVVCIFGDVSSALIGLRNLVMPLRASNFH 800
801 YHELKHIVFVGSIEYLKREWETLHNFPKVSILPGTPLSRADLRAVNINLC 850
851 DMCVILSANQNNIDDTSLQDKECILASLNIKSMQFDDSIGVLQANSQGFT 900
901 PPGMDRSSPDNSPVHGMLRQPSITTGVNIPIITELVNDTNVQFLDQDDDD 950
951 DPDTELYLTQPFACGTAFAVSVLDSLMSATYFNDNILTLIRTLVTGGATP 1000
1001 ELEALIAEENALRGGYSTPQTLANRDRCRVAQLALLDGPFADLGDGGCYG 1050
1051 DLFCKALKTYNMLCFGIYRLRDAHLSTPSQCTKRYVITNPPYEFELVPTD 1100
1101 LIFCLMQFDHNAGQSRASLSHSSHSSQSSSKKSSSVHSIPSTANRQNRPK 1150
1151 SRESRDKQKYVQEERL 1166
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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