 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P22367 from www.uniprot.org...
The NucPred score for your sequence is 0.15 (see score help below)
1 MHSAATSTYPSGKTSPAPVGTPGTEYSEYEFSNDVAVVGMACRVAGGNHN 50
51 PELLWQSLLSQKSAMGEIPPMRWEPYYRRDARNEKFLKNTTSRGYFLDRL 100
101 EDFDCQFFGISPKEAEQMDPQQRVSLEVASEALEDAGIPAKSLSGSDTAV 150
151 FWGVNSDDYSKLVLEDLPNVEAWMGIGTAYCGVPNRISYHLNLMGPSTAV 200
201 DAACASSLVAIHHGVQAIRLGESKVAIVGGVNALCGPGLTRVLDKAGAIS 250
251 SDGSCKSFDDDAHGYARGEGAGALVLKSLHRALLDHDNVLAVIKGSAVCQ 300
301 DGKTNGIMAPNSVAQQLAANNALSAANIDPHTVRYVEAHATSTPLGDPTE 350
351 ISAIASVYGADRPADDPCYIGSIKPNIGHLEAGAGVMGFIKAVLAIQKGV 400
401 LPPQANLTKLNSRIDWKTAGVKVVQEATPWPESDPIRRAGVCSYGYGGTV 450
451 SHAVIEEFSPILQPDPLGNGAVSGPGLLLLSGPQEKRLALQAKTLRDWMT 500
501 AEGKDHNLSDILTTLATRRDHHDYRAALVVDDYRDAEQVLQSLANGVDHT 550
551 FTTQSRVLGSDISKDVVWVFSGHGAQWPDMGKQLIHNPVFFAAIQPLDEL 600
601 IQAEIGLSPIELLRTGDFESSDRVQILTYVMQIGLSALLQSNGITPQAVI 650
651 GHSVGEIAASVVAGALSPAEGALIVTRRALLYRQVMGKGGMILVNLPSAE 700
701 TEEILGSRSDLVVAIDSSPSSCVVAGDKELVAETAEALKARGVKTFTVKS 750
751 DIAFHSPTLNGLVDPLRDVLAETLSPVSPNVKLYSTALADPRGQDLRDVE 800
801 YWAGNMVNRVRLTSAVKAAVEDGYRLFLEVSTHPVVSHSINETLMDAGME 850
851 DFAVIPTLLRKKPTEKHILHSIAQLHCRGAEVNWAAQMPGRWATGVPTTT 900
901 WMHKPIWRKIETAPLHTGLTHDVEKHTLLGQRIPVPGTDTYVYTTRLDND 950
951 TKPFPGSHPLHGTEIVPAAGLINTFLKGTGGQMLQNVVLRVPVAINAPRS 1000
1001 VQVVVQQDQVKVVSRLIPSEPSQLDDDASWVTHTTAYWDRKVAGSEDRID 1050
1051 FAAVKSRLVTKLADNFSIDYLDKVGVSAMGFPWAVTEHYRNDKEMLARVD 1100
1101 VNPAISGDAPLPWDSSSWAPVLDAATSVGSTIFPTPALRMPAQIERVEVF 1150
1151 TSQDPPKISWLYVQEASDSVPTSHVSVVSEAGEVLAKFTAMRFSEIEGTP 1200
1201 GVSGSMESLVHQIAWPPATPAEEPLSIETVILVSPDATTRALYAASLPTR 1250
1251 VNSFQFSSTQEFFSNASSLPLEKGTVVTYIPGEVASLAEVPAASESFTWN 1300
1301 LLELIKFTVNGSLPIKVFTLTANIGEGQTPTALAQSPLYGLARVIASEHP 1350
1351 DLGTLIDVEEPVIPLSTMRYIQGADIIRINDGIARTSRFRSLPRNKLLPA 1400
1401 SEGPRLLPRPEGTYLITGGLGVLGLEVADFLVEKGARRLLLISRRALPPR 1450
1451 RTWDQVSEDLQPTIAKIRLLESRGASVHVLPLDITKPDAVEQLTTALDRL 1500
1501 SLPSVQGVVHAAGVLDNELVMQTTRDAFNRVLAPKIAGALALHEVFPPKS 1550
1551 VDFFVMFSSCGNLVGFTGQASYGSGNAFLDTLATHRARLGDAAVSFQWTS 1600
1601 WRGLGMGASTDFINAELESKGITDVTRDEAFAAWQHLAKYDMDHGVVLRS 1650
1651 RAFEDGEPIPVSILNDIAVRRVGTVSNTSPAAAGSSDAVPTSGPELKAYL 1700
1701 DEKIRGCVAKVLQMTAEDVDSKAALADLGVDSVMTVTLRRQLQLTLKIAV 1750
1751 PPTLTWSHPTVSHLAVWFAEKLAK 1774
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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