 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q64487 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MVPVARPLSLLLTFFLCACAETPPRFTRTPVDQTGVSGGVASFICQATGD 50
51 PRPKIVWNKKGKKVSNQRFEVIEFDDGSGSVLRIQPLRTPRDEAIYECVA 100
101 SNNVGEISVSTRLTVLREDQIPRGFPTIDMGPQLKVVERTRTATMLCAAS 150
151 GNPDPEITWFKDFLPVDTSNNNGRIKQLRSESIGGTPIRGALQIEQSEES 200
201 DQGKYECVATNSAGTRYSAPANLYVRELREVRRVPPRFSIPPTNHEIMPG 250
251 GSVNITCVAVGSPMPYVKWMLGAEDLTPEDDMPIGRNVLELNDVRQSANY 300
301 TCVAMSTLGVIEAIAQITVKALPKPPGTPVVTESTATSITLTWDSGNPEP 350
351 VSYYIIQHKPKNSEEPYKEIDGIATTRYSVAGLSPYSDYEFRVVAVNNIG 400
401 RGPASEPVLTQTSEQAPSSAPRDVQARMLSSTTILVQWKEPEEPNGQIQG 450
451 YRVYYTMDPTQHVNNWMKHNVADSQITTIGNLVPQKTYSVKVLAFTSIGD 500
501 GPLSSDIQVITQTGVPGQPLNFKAEPESETSILLSWTPPRSDTIASYELV 550
551 YRDGDQGEEQRITIEPGTSYRLQGLKPNSLYYFRLSARSPQGLGASTAEI 600
601 SARTMQSKPSAPPQDISCTSPSSTSILVSWQPPPVEKQNGIITEYSLKYA 650
651 AVDGEDFKPHEILGIPSDTTKYLLEQLEKWTEYRITVTAHTDVGPGPESL 700
701 SVLIRTDEDVPSGPPRKVEVEAVNATAVKVSWRSPVPNKQHGQIRGYQVH 750
751 YVKMENGEPKGQPMLKDVMLADAQWEFDDTTEHDMIISGLQPETSYSLTV 800
801 TAYTTKGDGARSKPKLVSTTGAVPGKPRLVINHTQMNTALIQWHPPVDTF 850
851 GPLQGYRLKFGRKDMEPLTTLEFSEKEDHFTATDIHKGASYVFRLSARNK 900
901 VGFGEEMVKEISVPEEIPTGFPQNLHSEGTTSTSVQLSWQPPVLAERNGV 950
951 ITKYTLLYRDINVPLLPMEHLIVPADTSMTLTGLKSDTTYDVKVRAHTSK 1000
1001 GPGPYSPSVQFRTLPVDQVFAKNFHVKAVMKTSVLLSWEIPENYNSAMPF 1050
1051 KILYDDGKMVEEVDGRATQKLIVNLKPEKSYSFVLTNRGNSAGGLQHRVT 1100
1101 AKTAPDVLRTKPAFIGKTNLDGMITVQLPDVPANENIKGYYIIIVPLKKS 1150
1151 RGKFIKPWESPDEMELDELLKEISRKRRSIRYGREVELKPYIAAHFDVLP 1200
1201 TEFTLGDDKHYGGFTNKQLQSGQEYVFFVLAVMDHAESKMYATSPYSDPV 1250
1251 VSMDLDPQPITDEEEGLIWVVGPVLAVVFIICIVIAILLYKRKRAESESR 1300
1301 KSSLPNSKEVPSHHPTDPVELRRLNFQTPGMASHPPIPILELADHIERLK 1350
1351 ANDNLKFSQEYESIDPGQQFTWEHSNLEVNKPKNRYANVIAYDHSRVLLS 1400
1401 AIEGIPGSDYVNANYIDGYRKQNAYIATQGSLPETFGDFWRMIWEQRSAT 1450
1451 VVMMTKLEERSRVKCDQYWPSRGTETHGLVQVTLLDTVELATYCVRTFAL 1500
1501 YKNGSSEKREVRQFQFTAWPDHGVPEHPTPFLAFLRRVKTCNPPDAGPMV 1550
1551 VHCSAGVGRTGCFIVIDAMLERIKHEKTVDIYGHVTLMRAQRNYMVQTED 1600
1601 QYIFIHDALLEAVTCGNTEVPARNLYAYIQKLTQIETGENVTGMELEFKR 1650
1651 LASSKAHTSRFISANLPCNKFKNRLVNIMPYESTRVCLQPIRGVEGSDYI 1700
1701 NASFLDGYRQQKAYIATQGPLAETTEDFWRMLWEHNSTIVVMLTKLREMG 1750
1751 REKCHQYWPAERSARYQYFVVDPMAEYNMPQYILREFKVTDARDGQSRTV 1800
1801 RQFQFTDWPEQGVPKSGEGFIDFIGQVHKTKEQFGQDGPISVHCSAGVGR 1850
1851 TGVFITLSIVLERMRYEGVVDIFQTVKMLRTQRPAMVQTEDQYQFCYRAA 1900
1901 LEYLGSFDHYAT 1912
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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