 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9WTN3 from www.uniprot.org...
The NucPred score for your sequence is 0.64 (see score help below)
1 MDELAFGEAALEQTLAEMCELDTAVLNDIEDMLQLINNQDSDFPGLFDAP 50
51 YAGGETGDTGPSSPGANSPESFSSASLASSLEAFLGGPKVTPAPLSPPPS 100
101 APAALKMYPSVSPFSPGPGIKEEPVPLTILQPAAPQPSPGTLLPPSFPAP 150
151 PVQLSPAPVLGYSSLPSGFSGTLPGNTQQPPSSLPLAPAPGVLPTPALHT 200
201 QVQSLASQQPLPASAAPRTNTVTSQVQQVPVVLQPHFIKADSLLLTAVKT 250
251 DAGATVKTAGISTLAPGTAVQAGPLQTLVSGGTILATVPLVVDTDKLPIH 300
301 RLAAGSKALGSAQSRGEKRTAHNAIEKRYRSSINDKIVELKDLVVGTEAK 350
351 LNKSAVLRKAIDYIRFLQHSNQKLKQENLTLRSAHKSKSLKDLVSACGSG 400
401 GGTDVSMEGMKPEVVETLTPPPSDAGSPSQSSPLSFGSRASSSGGSDSEP 450
451 DSPAFEDSQVKAQRLPSHSRGMLDRSRLALCVLAFLCLTCNPLASLFGWG 500
501 ILTPSDATGTHRSSGRSMLEAESRDGSNWTQWLLPPLVWLANGLLVLACL 550
551 ALLFVYGEPVTRPHSGPAVHFWRHRKQADLDLARGDFPQAAQQLWLALQA 600
601 LGRPLPTSNLDLACSLLWNLIRHLLQRLWVGRWLAGQAGGLLRDRGLRKD 650
651 ARASARDAAVVYHKLHQLHAMGKYTGGHLAASNLALSALNLAECAGDAIS 700
701 MATLAEIYVAAALRVKTSLPRALHFLTRFFLSSARQACLAQSGSVPLAMQ 750
751 WLCHPVGHRFFVDGDWAVHGAPPESLYSVAGNPVDPLAQVTRLFREHLLE 800
801 RALNCIAQPSPGAADGDREFSDALGYLQLLNSCSDAAGAPACSFSVSSSM 850
851 AATTGPDPVAKWWASLTAVVIHWLRRDEEAAERLYPLVEHIPQVLQDTER 900
901 PLPRAALYSFKAARALLDHRKVESSPASLAICEKASGYLRDSLASTPTGS 950
951 SIDKAMQLLLCDLLLVARTSLWQRQQSPASVQVAHGTSNGPQASALELRG 1000
1001 FQHDLSSLRRLAQSFRPAMRRVFLHEATARLMAGASPARTHQLLDRSLRR 1050
1051 RAGSSGKGGTTAELEPRPTWREHTEALLLASCYLPPAFLSAPGQRMSMLA 1100
1101 EAARTVEKLGDHRLLLDCQQMLLRLGGGTTVTSS 1134
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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