 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O08532 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MAAGCLLALTLTLFQSGLIGPSSEEPFPSPVTIKSWVDKMQEDLVTLAKT 50
51 ASGVTQLADIYEKYQDLYTVEPNNARQLVEIAARDIEKLLSNRSKALVRL 100
101 AMEAEKVQAAHQWREDFASNEVVYYNAKDDLDPERNESEPGSQRIKPVFI 150
151 EDANFGRQISYQHAAVHIPTDIYEGSTIVLNELNWTSALDEVFKRNRDED 200
201 PTLLWQVFGSATGLARYYPASPWVDNSRTPNKIDLYDVRRRPWYIQGAAS 250
251 PKDMLILVDVSGSVSGLTLKLIRTSVSEMLETLSDDDFVNVASFNSNAQD 300
301 VSCFQHLVQANVRNKKVLKDAVNNITAKGITDYKKGFSFAFEQLLNYNVS 350
351 RANCNKIIMLFTDGGEERAQEIFAKYNKDKKVRVFTFSVGQHNYDRGPIQ 400
401 WMACENKGYYYEIPSIGAIRINTQEYLDVLGRPMVLAGDKAKQVQWTNVY 450
451 LDALELGLVITGTLPVFNVTGQSENKTNLKNQLILGVMGVDVSLEDIKRL 500
501 TPRFTLCPNGYYFAIDPNGYVLLHPNLQPKPIGVGIPTINLRKRRPNVQN 550
551 PKSQEPVTLDFLDAELENEIKVEIRNKMIDGESGEKTFRTLVKSQDERYI 600
601 DKGNRTYTWTPVNGTDYSLALVLPTYSFYYIKAKLEETITQARYSETLKP 650
651 DNFEESGYTFIAPREYCNDLKPSDNNTEFLLNFNEFIDRKTPNNPSCNTD 700
701 LINRILLDAGFTNELVQNYWSKQKNIKGVKARFVVTDGGITRVYPKEAGE 750
751 NWQENPETYEDSFYKRSLDNDNYVFTAPYFNKSGPGAYESGIMVSKAVEL 800
801 YIQGKLLKPAVVGIKIDVNSWIENFTKTSIRDPCAGPVCDCKRNSDVMDC 850
851 VILDDGGFLLMANHDDYTNQIGRFFGEIDPSMMRHLVNISLYAFNKSYDY 900
901 QSVCDPGAAPKQGAGHRSAYVPSIADILQIGWWATAAAWSILQQLLLSLT 950
951 FPRLLEAVEMEEDDFTASLSKQSCITEQTQYFFKNDTKSFSGLLDCGNCS 1000
1001 RIFHVEKLMNTNLVFIMVESKGTCPCDTRLLMQAEQTSDGPDPCDMVKQP 1050
1051 RYRKGPDVCFDNNVLEDYTDCGGVSGLNPSLWSIFGLQFILLWLVSGSRH 1100
1101 YLL 1103
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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