 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P15390 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MASSSLPNLVPPGPHCLRPFTPESLAAIEQRAVEEEARLQRNKQMEIEEP 50
51 ERKPRSDLEAGKNLPLIYGDPPPEVIGIPLEDLDPYYSDKKTFIVLNKGK 100
101 AIFRFSATPALYLLSPFSIVRRVAIKVLIHALFSMFIMITILTNCVFMTM 150
151 SNPPSWSKHVEYTFTGIYTFESLIKMLARGFCIDDFTFLRDPWNWLDFSV 200
201 ITMAYVTEFVDLGNISALRTFRVLRALKTITVIPGLKTIVGALIQSVKKL 250
251 SDVMILTVFCLSVFALVGLQLFMGNLRQKCVRWPPPMNDTNTTWYGNDTW 300
301 YSNDTWYGNDTWYINDTWNSQESWAGNSTFDWEAYINDEGNFYFLEGSND 350
351 ALLCGNSSDAGHCPEGYECIKAGRNPNYGYTSYDTFSWAFLALFRLMTQD 400
401 YWENLFQLTLRAAGKTYMIFFVVIIFLGSFYLINLILAVVAMAYAEQNEA 450
451 TLAEDQEKEEEFQQMLEKYKKHQEELEKAKAAQALESGEEADGDPTHNKD 500
501 CNGSLDASGEKGPPRPSCSADSAISDAMEELEEAHQKCPPWWYKCAHKVL 550
551 IWNCCAPWVKFKHIIYLIVMDPFVDLGITICIVLNTLFMAMEHYPMTEHF 600
601 DNVLSVGNLVFTGIFTAEMVLKLIAMDPYEYFQQGWNIFDSFIVTLSLVE 650
651 LGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLV 700
701 LAIIVFIFAVVGMQLFGKSYKECVCKIASDCNLPRWHMNDFFHSFLIVFR 750
751 ILCGEWIETMWDCMEVAGQAMCLTVFLMVMVIGNLVVLNLFLALLLSSFS 800
801 ADSLAASDEDGEMNNLQIAIGRIKWGIGFAKTFLLGLLRGKILSPKEIIL 850
851 SLGEPGGAGENAEESTPEDEKKEPPPEDKELKDNHILNHVGLTDGPRSSI 900
901 ELDHLNFINNPYLTIQVPIASEESDLEMPTEEETDAFSEPEDIKKPLQPL 950
951 YDGNSSVCSTADYKPPEEDPEEQAEENPEGEQPEECFTEACVKRCPCLYV 1000
1001 DISQGRGKMWWTLRRACFKIVEHNWFETFIVFMILLSSGALAFEDIYIEQ 1050
1051 RRVIRTILEYADKVFTYIFILEMLLKWVAYGFKVYFTNAWCWLDFLIVDV 1100
1101 SIISLVANWLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALLGAI 1150
1151 PSIMNVLLVCLIFWLIFSIMGVNLFAGKFYYCVNTTTSERFDISVVNNKS 1200
1201 ESESLMYTGQVRWMNVKVNYDNVGLGYLSLLQVATFKGWMDIMYAAVDSR 1250
1251 EKEEQPHYEVNLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKFGG 1300
1301 KDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPQNKIQGMVYDFVTKQVFDI 1350
1351 SIMILICLNMVTMMVETDDQSQLKVDILYNINMVFIIIFTGECVLKMFAL 1400
1401 RHYYFTIGWNIFDFVVVILSIVGLALSDLIQKYFVSPTLFRVIRLARIGR 1450
1451 VLRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMSNFAYV 1500
1501 KKESGIDDMFNFETFGNSIICLFEITTSAGWDGLLNPILNSGPPDCDPTL 1550
1551 ENPGTNVRGDCGNPSIGICFFCSYIIISFLIVVNMYIAIILENFNVATEE 1600
1601 SSEPLSEDDFEMFYETWEKFDPDATQFIDYSRLSDFVDTLQEPLKIAKPN 1650
1651 KIKLITLDLPMVPGDKIHCLDILFALTKEVLGDSGEMDALKQTMEEKFMA 1700
1701 ANPSKVSYEPITTTLKRKQEEVCAIKIQRAYRRHLLQRSVKQASYMYRHS 1750
1751 QDGNDDGAPEKEGLLANTMNKMYGHEKEGDGVQSQGEEEKASTEDAGPTV 1800
1801 EPEPTSSSDTALTPSPPPLPPSSSPPQGQTVRPGVKESLV 1840
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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