 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q60416 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MDELPFGEAAVEQALDELGELDAALLTDIQDMLQLINNQDSDFPGLFDSP 50
51 YAGGGAGDTEPTSPGANSPESLSSPASLGSSLEAFLGEPKATPASLSPVP 100
101 SASTALKMYPSVPPFSPGPGIKEEPVPLTILQPPAAQPSPGTLLPPSFPP 150
151 PPLQLSPAPVLGYSSLPSGFSGTLPGNTQQPPSSLSLASAPGVSPISLHT 200
201 QVQSSASQQPLPASTAPRTTTVTSQIQRVPVVLQPHFIKADSLLLTTVKT 250
251 DTGATMKTAGISTLAPGTAVQAGPLQTLVSGGTILATVPLVVDTDKLPIH 300
301 RLAAGSKALGSAQSRGEKRTAHNAIEKRYRSSINDKIVELKDLVVGTEAK 350
351 LNKSAVLRKAIDYIRFLQHSNQKLKQENLALRNAAHKSKSLKDLVSACGS 400
401 AGGTDVAMEGVKPEVVDTLTPPPSDAGSPSQSSPLSLGSRGSSSGGSDSE 450
451 PDSPVFEDSQVKAQRLHSHGMLDRSRLALCALVFLCLTCNPLASLFGWGI 500
501 PGPSSASGAHHSSGRSMLEAESRDGSNWTQWLLPPLVWLANGLLVLACLA 550
551 LLFVYGEPVTRPHTSPAVHFWRHRKQADLDLARGDFAQAAQQLWLALQAL 600
601 GRPLPTSNLDLACSLLWNLIRHLLQRLWVGRWLAGRAGGLRRDCGLRMDA 650
651 RASARDAALVYHKLHQLHAMGKYTGGHLIASNLALSALNLAECAGDAVSM 700
701 ATLAEIYVAAALRVKTSLPRALHFLTRFFLSSARQACLAQSGSVPLAMQW 750
751 LCHPVGHRFFVDGDWAVHGAPQESLYSVAGNPVDPLAQVTRLFCEHLLER 800
801 ALNCIAQPSPGTADGDREFSDALGYLQLLNRCSDAVGTPACSFSVSSSMA 850
851 STTGTDPVAKWWASLTAVVIHWLRRDEEAAERLYPLVERMPHVLQETERP 900
901 LPKAALYSFKAARALLDHRKVESGPASLAICEKASGYLRDSLAAPPTGSS 950
951 IDKAMQLLLCDLLLVARTSMWQRQQSPASAQVAHSASNGSQASALELRGF 1000
1001 QQDLSSLRRLAQNFRPAMRRVFLHEATARLMAGASPARTHQLLDRSLRRR 1050
1051 AGSSGKGGTVAELEPRPTWREHTEALLLASCYLPPAFLSAPGQQMSMLAE 1100
1101 AARTVEKLGDHRLLLDCQQMLLRLGGGTTVTSS 1133
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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