 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8IZX4 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MRPGCDLLLRAAATVTAAIMSDSDSEEDSSGGGPFTLAGILFGNISGAGQ 50
51 LEGESVLDDECKKHLAGLGALGLGSLITELTANEELTGTGGALVNDEGWI 100
101 RSTEDAVDYSDINEVAEDESQRHQQTMGSLQPLYHSDYDEDDYDADCEDI 150
151 DCKLMPPPPPPPGPMKKDKDQDAITCVSESGEDIILPSIIAPSFLASEKV 200
201 DFSSYSDSESEMGPQEATQAESEDGKLTLPLAGIMQHDATKLLPSVTELF 250
251 PEFRPGKVLRFLHLFGPGKNVPSVWRSARRKRKKHRELIQEEQIQEVECS 300
301 VESEVSQKSLWNYDYAPPPPPEQCLADDEITMMVPVESKFSQSTGDVDKV 350
351 TDTKPRVAEWRYGPARLWYDMLGVSEDGSGFDYGFKLRKTQHEPVIKSRM 400
401 MEEFRKLEESNGTDLLADENFLMVTQLHWEDSIIWDGEDIKHKGTKPQGA 450
451 SLAGWLPSIKTRNVMAYNVQQGFAPTLDDDKPWYSIFPIDNEDLVYGRWE 500
501 DNIIWDAQAMPRLLEPPVLALDPNDENLILEIPDEKEEATSNSPSKESKK 550
551 ESSLKKSRILLGKTGVIREEPQQNMSQPEVKDPWNLSNDEYYFPKQQGLR 600
601 GTFGGNIIQHSIPAMELWQPFFPTHMGPIKIRQFHRPPLKKYSFGALSQP 650
651 GPHSVQPLLKHIKKKAKMREQERQASGGGELFFMRTPQDLTGKDGDLILA 700
701 EYSEENGPLMMQVGMATKIKNYYKRKPGKDPGAPDCKYGETVYCHTSPFL 750
751 GSLHPGQLLQALENNLFRAPVYLHKMPETDFLIIRTRQGYYIRELVDIFV 800
801 VGQQCPLFEVPGPNSRRANMHIRDFLQVFIYRLFWKSKDRPRRIRMEDIK 850
851 KAFPSHSESSIRKRLKLCADFKRTGMDSNWWVLKSDFRLPTEEEIRAKVS 900
901 PEQCCAYYSMIAAKQRLKDAGYGEKSFFAPEEENEEDFQMKIDDEVHAAP 950
951 WNTTRAFIAAMKGKCLLEVTGVADPTGCGEGFSYVKIPNKPTQQKDDKEP 1000
1001 QAVKKTVTGTDADLRRLSLKNAKQLLRKFGVPEEEIKKLSRWEVIDVVRT 1050
1051 MSTEQAHSGEGPMSKFARGSRFSVAEHQERYKEECQRIFDLQNKVLSSTE 1100
1101 VLSTDTDSISAEDSDFEEMGKNIENMLQNKKTSSQLSREWEEQERKELRR 1150
1151 MLLVAGSAASGNNHRDDVTASMTSLKSSATGHCLKIYRTFRDEEGKEYVR 1200
1201 CETVRKPAVIDAYVRIRTTKDEKFIQKFALFDEKHREEMRKERRRIQEQL 1250
1251 RRLKRNQEKEKLKGPPEKKPKKMKERPDLKLKCGACGAIGHMRTNKFCPL 1300
1301 YYQTNVPPSKPVAMTEEQEEELEKTVIHNDNEELIKVEGTKIVFGKQLIE 1350
1351 NVHEVRRKSLVLKFPKQQLPPKKKRRVGTTVHCDYLNIPHKSIHRRRTDP 1400
1401 MVTLSSILESIINDMRDLPNTHPFHTPVNAKVVKDYYKIITRPMDLQTLR 1450
1451 ENVRKCLYPSREEFREHLELIVKNSATYNGPKHSLTQISQSMLDLCDEKL 1500
1501 KEKEDKLARLEKAINPLLDDDDQVAFSFILDNIVTQKMMAVPDSWPFHHP 1550
1551 VNKKFVPDYYKMIVNPVDLETIRKNISKHKYQSRESFLDDVNLILANSVK 1600
1601 YNGPESQYTKTAQEIVNICYQTITEYDEHLTQLEKDICTAKEAALEEAEL 1650
1651 ESLDPMTPGPYTSQPPDMYDTNTSLSTSRDASVFQDESNLSVLDISTATP 1700
1701 EKQMCQGQGRLGEEDSDVDVEGYDDEEEDGKPKPPAPEGGDGDLADEEEG 1750
1751 TVQQPEASVLYEDLLISEGEDDEEDAGSDEEGDNPFSAIQLSESGSDSDV 1800
1801 GYGGIRPKQPFMLQHASGEHKDGHGK 1826
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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