 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q2EMV9 from www.uniprot.org...
The NucPred score for your sequence is 0.54 (see score help below)
1 MAASGSFPLLVEGSWGPDPPKNLINKLQVYFQSRKKSGGGECEVVPEPGN 50
51 PARFRVLFSPEDVRQNVLERGNHELVWQEKGTFKLTVLMPTDPEEASASK 100
101 KSRKESPEEESKTKEDAVKQGDLDITHSPSSGSEKTEDVPKECENISSMV 150
151 AFENLPEKVSEMVLTILVENISGLPSDDFKVEVNRDFAVAVVTFQKPIDI 200
201 KKFIVDCISHRSNQQLQLAPRLLETTNVVRVENLPPGVDEYQLQLFFENP 250
251 FNGGGRVARVECFPEESSALVEFCDSKVLDTVMAKTHSYNKMPLSVFPYY 300
301 PSLGTALYGEEKPLIKLPASFQESLDLPLWKFFQKNNHLIEEINNEMRCC 350
351 HCELTWSEINGKLTIRPAATLVNHRLSIKTWQRDASAVLSGIKSKYGVEL 400
401 FEVCSPVWDIIKHELESGDDRVLVEFEKESLNIAGKSEDVQGMSQKIREL 450
451 IESTTEKLRREEQSLKEKVAISPGKHYLLHHSGFLKDLSKGFPEMEISYD 500
501 ATAQFLYLKGFRADVYKVKCDIQEKVFSMAQKDVQVSSEVFEFLQQVDSQ 550
551 RLSKSLFEAQNILAIYELKGTALFLVGSSFKDLAEAETKMLSALSHKQIE 600
601 VEDKEVLISNGWKKKVHPLQKRHSSCATIIVQNELTSETPAKVIVTGCVK 650
651 EVNEIHRQLFEYLENNMKVERALKIKPSLIVDYLRTDKRLLSKIKKAHVY 700
701 VHFKPKDNPNSILLTGCKSKVLECMNLVKEIQDSVCVQRFQTDKAGVRHF 750
751 FKDKESYYKTEIGRQFGCVIELEEDREEKGEEEDGEEEEGEEEGESSINE 800
801 QKCHLQRDIAPGVKLFVLEEDLSRFPVDVVVNAANENLKHISGLAQALSK 850
851 AAGPELQTECDQIVKEGGVVLPGNAVISKAGKLPCHHVIHAVGPRWKGDK 900
901 VLECVSLLKKVVRQSLSLAEEHRCRSIAMPAVSAGIFDFPLELCVANIVS 950
951 AIKEHFQHKRDTHTLKKIYLVGLPAKVARAFAEAVKTTYKDSLSHTAFPS 1000
1001 SLKALVPLGKTPQKQGSLLVSPEGLRIRLVEEGVQNATTHAIVNSISPDL 1050
1051 KLNKGPLSQAFLEKAGPKLQEELTRSGQGVSVDVGTILQTSGCNLNSRHV 1100
1101 FHVVPPPWKSNNSAWSLKIMKNIIRDCLKTTENLSLQSIAFPAIGTGNLR 1150
1151 FPKPEFAKLIISEVLKFSSRNQLKTLQEVQFLLHPKDHENIQAFSDEFDK 1200
1201 RNNGDPSDKNPKAEDTQGIYGSLSSPTLGMHEMNIGPILFQVATGNIIKE 1250
1251 VADVIVNSTTLTFDLKSGVSKAILEGAGQNVEQECSLLAKQSNHGYIVTG 1300
1301 GGLLQCKNIIHVVGGNDVKKSVSCVLEECEQRNYSSICLPAIGTGNAQQD 1350
1351 PNVVAKAIIDAIEEFVQKKSVQAVKRVKVVIFQPHILQFFYDNMKEREGS 1400
1401 PAPPKQSPAKQSVMSKIASFLGFPKQASPKKNTLVLEKKIEHTVFQVCGS 1450
1451 GVDSVNKTISWLKELITKEQLSYTNDDECVSDFDMEEYEKLNEIQKELNI 1500
1501 TIEMNQKKTSIQVSGISRDVIKARDEIEGMIKSIRLAKEKESQADYISTY 1550
1551 VEWQYIDKNITQCFDKMTNMKLEVAWKAKKKDTVVQIHNQDFTVDLSTNT 1600
1601 ATAPQGQTFTVQRLVKAEAEIPANWSDMKQDKLLLVNLQTSDPEYNMVAS 1650
1651 AFRQTCSNFFIEKIERIQNPALWRRYQAYKKSMDEKNGNVRNEKHLFHGT 1700
1701 EASSLPQLNSNGFNRSYAGKNATAYGKGTYFAVKASYSACDTYSRPDTNG 1750
1751 RKYMYYVRVLTGNYTNGNASLIVPPSRDPQNAADLYDTVTDNDKNPSIFV 1800
1801 VFYDNQTYPEYLITFRQ 1817
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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