 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38929 from www.uniprot.org...
The NucPred score for your sequence is 0.38 (see score help below)
1 MSRQDENSALLANNENNKPSYTGNENGVYDNFKLSKSQLSDLHNPKSIRS 50
51 FVRLFGYESNSLFKYLKTDKNAGISLPEISNYRKTNRYKNYGDNSLPERI 100
101 PKSFLQLVWAAFNDKTMQLLTVAAVVSFVLGLYELWMQPPQYDPEGNKIK 150
151 QVDWIEGVAIMIAVFVVVLVSAANDYQKELQFAKLNKKKENRKIIVIRND 200
201 QEILISIHHVLVGDVISLQTGDVVPADCVMISGKCEADESSITGESNTIQ 250
251 KFPVDNSLRDFKKFNSIDSHNHSKPLDIGDVNEDGNKIADCMLISGSRIL 300
301 SGLGRGVITSVGINSVYGQTMTSLNAEPESTPLQLHLSQLADNISVYGCV 350
351 SAIILFLVLFTRYLFYIIPEDGRFHDLDPAQKGSKFMNIFITSITVIVVA 400
401 VPEGLPLAVTLALAFATTRMTKDGNLVRVLRSCETMGSATAVCSDKTGTL 450
451 TENVMTVVRGFPGNSKFDDSKSLPVSEQRKLNSKKVFEENCSSSLRNDLL 500
501 ANIVLNSTAFENRDYKKNDKNTNGSKNMSKNLSFLDKCKSRLSFFKKGNR 550
551 EDDEDQLFKNVNKGRQEPFIGSKTETALLSLARLSLGLQPGELQYLRDQP 600
601 MEKFNIEKVVQTIPFESSRKWAGLVVKYKEGKNKKPFYRFFIKGAAEIVS 650
651 KNCSYKRNSDDTLEEINEDNKKETDDEIKNLASDALRAISVAHKDFCECD 700
701 SWPPEQLRDKDSPNIAALDLLFNSQKGLILDGLLGIQDPLRAGVRESVQQ 750
751 CQRAGVTVRMVTGDNILTAKAIARNCAILSTDISSEAYSAMEGTEFRKLT 800
801 KNERIRILPNLRVLARSSPEDKRLLVETLKGMGDVVAVTGDGTNDAPALK 850
851 LADVGFSMGISGTEVAREASDIILMTDDFSAIVNAIKWGRCVSVSIKKFI 900
901 QFQLIVNITAVILTFVSSVASSDETSVLTAVQLLWINLIMDTLAALALAT 950
951 DKPDPNIMDRKPRGRSTSLISVSTWKMILSQATLQLIVTFILHFYGPELF 1000
1001 FKKHEDEITSHQQQQLNAMTFNTFVWLQFFTMLVSRKLDEGDGISNWRGR 1050
1051 ISAANLNFFQDLGRNYYFLTIMAIIGSCQVLIMFFGGAPFSIARQTKSMW 1100
1101 ITAVLCGMLSLIMGVLVRICPDEVAVKVFPAAFVQRFKYVFGLEFLRKNH 1150
1151 TGKHDDEEALLEESDSPESTAFY 1173
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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