 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P48754 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MDLSAVQIQEVQNVLHAMQKILECPICLELIKEPVSTKCDHIFCKFCMLK 50
51 LLNQKKGPSQCPLCKNEITKRSLQGSTRFSQLAEELLRIMAAFELDTGMQ 100
101 LTNGFSFSKKRNNSCERLNEEASIIQSVGYRNRVRRLPQVEPGNATLKDS 150
151 LGVQLSNLGIVRSVKKNRQTQPRKKSVYIELDSDSSEETVTKPGDCSVRD 200
201 QELLQTAPQEAGDEGKLHSAEEAACEFSEGIRNIEHHQCSDDLNPTENHA 250
251 TERHPEKCQSISISNVCVEPCGTDAHASSLQPETSSLLLIEDRMNAEKAE 300
301 FCNKSKQPGIAVSQQSRWAASKGTCNDRQVPSTGEKVGPNADSLSDREKW 350
351 THPQSLCPENSGATTDVPWITLNSSVQKVNEWFSRTGEMLTSDSASARRH 400
401 ESNAEAAVVLEVSNEVDGGFSSSRKTDLVTPDPHHTLMCKSGRDFSKPVE 450
451 DNISDKIFGKSYQRKGSRPHLNHVTEIIGTFITEPQITQEQPFTNKLKRK 500
501 RSTSLQPEDFIKKADSAGVQRTPDNINQGTDLMEPNEQAVSTTSNCQENK 550
551 IAGSNLQKEKSAHPTESLRKEPASTAGAKSISNSVSDLEVELNVHSSKAP 600
601 KKNRLRRKSSIRCALPLEPISRNPSPPTCAELQIDSCGSSEETKKNHSNQ 650
651 QPAGHLREPQLIEDTEPAADAKKNEPNEHIRKRRASDAFPEEKLMNKAGL 700
701 LTSCSSPRKSQGPVNPSPQRTGTEQLETRQMSDSAKELGDRVLGGEPSGK 750
751 TTDRSEESTSVSLVSDTDYDTQNSVSVLDAHTVRYARTGSAQCMTQFVAS 800
801 ENPKELVHGSNNAGSGTEGLKPPLRHALNLSQEKVEMEDSELDTQYLQNT 850
851 FQVSKRQSFALFSKPRSPQKDCAHSVPSKELSPKVTAKGKQKERQGQEEF 900
901 EISHVQAVAATVGLPVPCQEGKLAADTMCDRGCRLCPSSHYRSGENGLSA 950
951 TGKSGISQNSHFKQSVSPIRSSIKTDNRKPLTEGRFERHTSSTEMAVGNE 1000
1001 NILQSTVHTVSLNNRGNACQEAGSGSIHEVCSTGDSFPGQLGRNRGPKVN 1050
1051 TVPPLDSMQPGVCQQSVPVSDKYLEIKKQEGEAVCADFSPCLFSDHLEQS 1100
1101 MSGKVFQVCSETPDDLLDDVEIQGHTSFGEGDIMERSAVFNGSILRRESS 1150
1151 RSPSPVTHASKSQSLHRASRKLESSEESDSTEDEDLPCFQHLLSRISNTP 1200
1201 ELTRCSSAVTQRMPEKAEGTQAPWKGSSSDCNNEVIMIEASQEHQFSEDP 1250
1251 RCSGSMFSSQHSAAQGSTANANSQDSNFIPPSKQRSHQCGNEEAFLSDKE 1300
1301 LISDNEEMATCLEEDNDQEEDSIIPDSEASGYESETNLSEDCSQSDILTT 1350
1351 QQRATMKYNLIKLQQEMAHLEAVLEQRGNQPSGHSPSLLADPCALEDLPD 1400
1401 LEPNMSGAAILTSKNINENPVSQNLKSACDDKFQLQHLEGPTSGDDESGM 1450
1451 GRPSPFKSPLAGSRGSAHGCSRHLQKRNSPSQEELLQPAGSEASSEPHNS 1500
1501 TGQSCLPRRELEGTPYLGSGISLFSSRDPESESPKEPAHIGTTPASTSAL 1550
1551 KIPQGQVAFRSAAAAGADKAVVGIVSKIKPELTSSEERADRDISMVVSGL 1600
1601 TPKEVMTVQKFAEKYRLTLTDAITEETTHVIIKTDAEFVCERTLKYFLGI 1650
1651 AGGKWIVSYSWVVRSIQERRLLNVHEFEVKGDVVTGRNHQGPRRSRESRE 1700
1701 KLFKGLQVYCCEPFTNMPKDELERMLQLCGASVVKELPSLTHDTGAHLVV 1750
1751 IVQPSAWTEDSNCPDIGQLCKARLVMWDWVLDSLSSYRCRDLDAYLVQNI 1800
1801 TCDSSEPQDSND 1812
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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