 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q01804 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MEAAVGVPDGGDQGGAGPREDATPMDAYLRKLGLYRKLVAKDGSCLFRAV 50
51 AEQVLHSQSRHVEVRMACIHYLRENREKFEAFIEGSFEEYLKRLENPQEW 100
101 VGQVEISALSLMYRKDFIIYREPNVSPSQVTENNFPEKVLLCFSNGNHYD 150
151 IVYPIKYKESSAMCQSLLYELLYEKVFKTDVSKIVMELDTLEVADEDNSE 200
201 ISDSEDDSCKSKTAAAAADVNGFKPLSGNEQLKNNGNSTSLPLSRKVLKS 250
251 LNPAVYRNVEYEIWLKSKQAQQKRDYSIAAGLQYEVGDKCQVRLDHNGKF 300
301 LNADVQGIHSENGPVLVEELGKKHTSKNLKAPPPESWNTVSGKKMKKPST 350
351 SGQNFHSDVDYRGPKNPSKPIKAPSALPPRLQHPSGVRQHAFSSHSSGSQ 400
401 SQKFSSEHKNLSRTPSQIIRKPDRERVEDFDHTSRESNYFGLSPEERREK 450
451 QAIEESRLLYEIQNRDEQAFPALSSSSVNQSASQSSNPCVQRKSSHVGDR 500
501 KGSRRRMDTEERKDKDSIHGHSQLDKRPEPSTLENITDDKYATVSSPSKS 550
551 KKLECPSPAEQKPAEHVSLSNPAPLLVSPEVHLTPAVPSLPATVPAWPSE 600
601 PTTFGPTGVPAPIPVLSVTQTLTTGPDSAVSQAHLTPSPVPVSIQAVNQP 650
651 LMPLPQTLSLYQDPLYPGFPCNEKGDRAIVPPYSLCQTGEDLPKDKNILR 700
701 FFFNLGVKAYSCPMWAPHSYLYPLHQAYLAACRMYPKVPVPVYPHNPWFQ 750
751 EAPAAQNESDCTCTDAHFPMQTEASVNGQMPQPEIGPPTFSSPLVIPPSQ 800
801 VSESHGQLSYQADLESETPGQLLHADYEESLSGKNMFPQPSFGPNPFLGP 850
851 VPIAPPFFPHVWYGYPFQGFIENPVMRQNIVLPSDEKGELDLSLENLDLS 900
901 KDCGSVSTVDEFPEARGEHVHSLPEASVSSKPDEGRTEQSSQTRKADTAL 950
951 ASIPPVAEGKAHPPTQILNRERETVPVELEPKRTIQSLKEKTEKVKDPKT 1000
1001 AADVVSPGANSVDSRVQRPKEESSEDENEVSNILRSGRSKQFYNQTYGSR 1050
1051 KYKSDWGYSGRGGYQHVRSEESWKGQPSRSRDEGYQYHRNVRGRPFRGDR 1100
1101 RRSGMGDGHRGQHT 1114
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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