 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P56785 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MMVFQSFILGNLVSLCMKIINSVVVVGLYYGFLTTFSIGPSYLFLLRARV 50
51 MDEGEEGTEKKVSATTGFIAGQLMMFISIYYAPLHLALGRPHTITVLALP 100
101 YLLFHFFWNNHKHFFDYGSTTRNEMRNLRIQCVFLNNLIFQLFNHFILPS 150
151 SMLARLVNIYMFRCNNKMLFVTSSFVGWLIGHILFMKWVGLVLVWIQQNN 200
201 SIRSNVVIRSNKYKFLVSELRNSMARIFSILLFITCVYYLGRIPSPIFTK 250
251 KLKGTSETGGTKQDQEVSTEEAPFPSLFSEEGEDLDKIDEMEEIRVNGKD 300
301 KINKDDEFHVRTYYNYKTVSENLYGNKENSNLEFFKIKKKEDHFLWFEKP 350
351 FVTLVFDYKRWNRPNRYIKNDKIENIVRNEMSQYFFYTCQSDGKERISFT 400
401 YPPNLSTFFEMIQKRIPSFTKEKKTFDQVSTYWSLIHEEKRENLKKEFLN 450
451 RIEALDKEWSVENILEKTTRFCYNEAKKEYLPKIYDPFLHGISRGRIKKL 500
501 PPFQIITETYRKNNLGGSWINKIHGLLLKINYKKFEQTIEKFNRKSLSIE 550
551 KKLSFFSEPQQEEKINSEEEIKTFKFLFDIVRTDSNDQTLIKNFMDFPEI 600
601 NKKVPRWSYKLISELEELEGENEENVPMEPGIRSRKAKRVVVFTDKEPHG 650
651 EIYTNLKDNQNSDQNDEMALIRYSQQSDFRREIIKGSMRSQRRKTVIWEF 700
701 FQAKVHSPLFFDRIDKLFFFSFDIWGLKKKIIKNFIWKKKIDKKEEEQSK 750
751 REETRRIEIAETWDSFLFAQIIRGSLLVTQSILRKYIILPLLIIIKNSVR 800
801 MLLFQFPEWSQDLKDWKREMHVKCTYNGVQLSETEFPRNWLTDGIQIKIL 850
851 FPFYLKPWHKSKFQASQKARLKKTKDKGEKNDFCFLTVWGMETELPFGSA 900
901 QRKPSFFEPISKELKKRIKKLKKKSFVVLKIFKERAPIFLKVAKETKNWI 950
951 LKNFIFIKGISKRNLIPLFGPREIYELNEPKKDSIISNQMIHELSVQNKS 1000
1001 LEWTNSSLSEKKIKNLIDRKKTIRNQIEEISKEKQNLTNSCTKLRYDSKI 1050
1051 IESSKKIWQTFKRKNTRLIRKSIFFFKFCIEQMSIAIFLGIINIPRITTQ 1100
1101 LFFESTKKILDKYIYKNEENGEKKKNTLYFISTIKNLISNKKKMSYDLCS 1150
1151 LSQAYVFYKLSQIKVSNFCKLKAVLEYNICITSFFVKNKIKVFFQEHGIF 1200
1201 HYELKNKTFLNSEVNQWKNWLRSQYQYNLPQISWARLVTQNWKNKINKDS 1250
1251 LVLNPSLTKEDSYEKKKFDNYKKQKFFEADSLLNPKHNVKKDSIYNLFCY 1300
1301 KSIHSTEKNFDMSIGIALDNCLVSSFLEKYNIRGMGEIRHRKYLDWRILN 1350
1351 FWFTKKVTIEPWVDTKSKKKYINTKVQNYQKIDKITQTDLANKKRNFFDW 1400
1401 MGMNEEILNQRITNFEFFFFPEFFLFSSTYKMKPWVIPIKLLLLNFNENI 1450
1451 NVNKKIIRKKKGFIPSNEKESLRFYNLNKEEKESAGQVELESDKETKRNP 1500
1501 EAARLNQEKNIEENFAESTIKKRKNKKQYKSNTEAELDLFLTRYSRFQLR 1550
1551 WNCFFNQKILNNVKVYCLLVRLNNPNEIAVSSIERGEMSLDILMIEKNFT 1600
1601 FAKLMKKGILIIEPVRLSVQNDGQLIIYRTIGISLVHKNKHKISKRYKKK 1650
1651 SYINKKFFEKSITKYQNKTVNKKKNNYDFFVPEKILSPKRRREFRILICF 1700
1701 NLKKKNARDTNSRFDKNIQNLTTVLHKKKDLDLDKDKNNLINLKSFLWPN 1750
1751 FKLEDLACMNRYWFNTTNGNHFSMIRIRMYTRFPIP 1786
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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