 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P2F8 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MSDPRQSQEEKHKLGRASSKFKDPPRIMQSDDYFARKFKAINGNMGPTTS 50
51 LNASNSNETGGGGPANGTPAVPKMGVRARVSEWPPKKDCSKELTCKALWE 100
101 SRSQTSYESITSVLQNGQSDQSEGQQDEQLDLDFVEAKYTIGDIFVHSPQ 150
151 RGLHPIRQRSNSDVTISDIDAEDVLDQNAVNPNTGAALHREYGSTSSIDR 200
201 QGLSGENFFAMLRGYRVENYDHKAMVPFGFPEFFRCDPAISPSLHAAAQI 250
251 SRGEFVRISGLDYVDSALLMGRDRDKPFKRRLKSESVETSLFRKLRTVKS 300
301 EHETFKFTSELEESRLERGIRPWNCQRCFAHYDVQSILFNINEAMATRAN 350
351 VGKRKNITTGASAASQTQMPTGQTGNCESPLGSKEDLNSKENLDADEGDG 400
401 KSNDLVLSCPYFRNETGGEGDRRIALSRANSSSFSSGESCSFESSLSSHC 450
451 TNAGVSVLEVPRENQPIHREKVKRYIIEHIDLGAYYYRKFFYGKEHQNYF 500
501 GIDENLGPVAVSIRREKVEDAKEKEGSQFNYRVAFRTSELTTLRGAILED 550
551 AIPSTARHGTARGLPLKEVLEYVIPELSIQCLRQASNSPKVSEQLLKLDE 600
601 QGLSFQHKIGILYCKAGQSTEEEMYNNETAGPAFEEFLDLLGQRVRLKGF 650
651 SKYRAQLDNKTDSTGTHSLYTTYKDYELMFHVSTLLPYMPNNRQQLLRKR 700
701 HIGNDIVTIVFQEPGALPFTPKSIRSHFQHVFVIVKVHNPCTENVCYSVG 750
751 VSRSKDVPPFGPPIPKGVTFPKSAVFRDFLLAKVINAENAAHKSEKFRAM 800
801 ATRTRQEYLKDLAENFVTTATVDTSVKFSFITLGAKKKEKVKPRKDAHLF 850
851 SIGAIMWHVIARDFGQSADIECLLGISNEFIMLIEKDSKNVVFNCSCRDV 900
901 IGWTSGLVSIKVFYERGECVLLSSVDNCAEDIREIVQRLVIVTRGCETVE 950
951 MTLRRNGLGQLGFHVNFEGIVADVEPFGFAWKAGLRQGSRLVEICKVAVA 1000
1001 TLTHEQMIDLLRTSVTVKVVIIQPHDDGSPRRGCSELCRIPMVEYKLDSE 1050
1051 GTPCEYKTPFRRNTTWHRVPTPALQPLSRASPIPGTPDRLPCQQLLQQAQ 1100
1101 AAIPRSTSFDRKLPDGTRSSPSNQSSSSDPGPGGSGPWRPQVGYDGCQSP 1150
1151 LLLEHQGSGPLECDGAREREDTMEASRHPETKWHGPPSKVLGSYKERALQ 1200
1201 KDGSCKDSPNKLSHIGDKSCSSHSSSNTLSSNTSSNSDDKHFGSGDLMDP 1250
1251 ELLGLTYIKGASTDSGIDTAPCMPATILGPVHLAGSRSLIHSRAEQWADA 1300
1301 ADVSGPDDEPAKLYSVHGYASTISAGSAAEGSMGDLSEISSHSSGSHHSG 1350
1351 SPSAHCSKSSGSLDSSKVYIVSHSSGQQVPGSMSKPYHRQGAVNKYVIGW 1400
1401 KKSEGSPPPEEPEVTECPGMYSEMDVMSTATQHQTVVGDAVAETQHVLSK 1450
1451 EDFLKLMLPDSPLVEEGRRKFSFYGNLSPRRSLYRTLSDESICSNRRGSS 1500
1501 FGSSRSSVLDQALPNDILFSTTPPYHSTLPPRAHPAPSMGSLRNEFWFSD 1550
1551 GSLSDKSKCADPGLMPLPDTATGLDWTHLVDAARAFEGLDSDEELGLLCH 1600
1601 HTSYLDQRVASFCTLTDMQHGQDLEGAQELPLCVDPGSGKEFMDTTGERS 1650
1651 PSPLTGKVNQLELILRQLQTDLRKEKQDKAVLQAEVQHLRQDNMRLQEES 1700
1701 QTATAQLRKFTEWFFTTIDKKS 1722
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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