 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P70207 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MEQRRFYLRAMQADNLSVVLLSVAWLLLARGTTGMPQYSTFHSENRDWTF 50
51 NHLTVHRRTGAVYVGAINRVYKLTGNLTIQVAHKTGPEEDNKACYPPLIV 100
101 QPCSEVLTLTNNVNKLLIIDYSENRLLACGSLYQGVCKLLRLDDLFILVE 150
151 PSHKKEHYLSSVNKTGTMYGVIVRSEGEDGKLFIGTAVDGKQDYFPTLSS 200
201 RKLPRDPESSAMLDYELHSDFVSSLIKIPSDTLALVSHFDIFYIYGFASG 250
251 GFVYFLTVQPETPDGMAINSAGDLFYTSRIVRLCKDDPKFHSYVSLPFGC 300
301 TRAGVEYRLLQAAYLAKPGEALAQAFNISSDEDVLFAIFSKGQKQYHHPP 350
351 DDSALCAFPIRAINLQIKERLQSCYHGEGNLELNWLLGKDVQCTKAPVPI 400
401 DDNFCGLDINQPLGGSTPVEGLTLYTTSRDRLTSVASYVYNGYSVVFVGT 450
451 KSGKLKKIRADGPPHGGVQYEMVSVFKDGSPILRDMAFSINQLYLYVMSE 500
501 RQVTRVPVESCEQYTTCGECLSSGDPHCGWCALHNMCSRRDKCQRAWEAN 550
551 RFAASISQCMSLEVHPNSISVSDHSRLLSLVVNDAPNLSEGIACAFGNLT 600
601 EVEGQVSGSQVICISPGPKDVPVIPLDQDWFGLELQLRSKETGKIFVSTE 650
651 FKFYNCSAHQLCLSCVNSAFRCHWCKYRNLCTHDPTTCSFQEGRINVSED 700
701 CPQLVPTEEILIPVGEVKPITLKARNLPQPQSGQRGYECVLSIQGAVHRV 750
751 PALRFNSSSVQCQNSSYQYDGMDISNLAVDFAVVWNGNFIIDNPQDLKVH 800
801 LYKCAAQRESCGLCLKADHKFECGWCSGERRCTLHQHCPSTSSPWLDWSS 850
851 HNVKCSNPQITEILTVSGPPEGGTRVTIHGVNLGLDFSEIAHHVQVAGVP 900
901 CTPIPGEYIIAEQIVCEMGHAVIGTTSGPVRLCIGECKPEFMTKSHQQYT 950
951 FVNPSVLSLSPIRGPESGGTMVTITGHYLGAGSSVAVYLGNQTCEFYGRS 1000
1001 MNEIVCVSPPSSNGLGPVPVSVSVDRARVDSSLQFEYIDDPRVQRIEPEW 1050
1051 SITSGHTPLTITGFNLDVIQEPRVRVKFNGKESVNVCTVVNTTTLTCLAP 1100
1101 SLTSDYRPGLDTVERPDEFGFLFNNVQSLLIYNDTKFIYYPNPTFELLSP 1150
1151 TGILDQKPGSPIILKGKNLCPPASGGAKLNYTVMIGETPCTVTVSETQLL 1200
1201 CEPPNLTGQHKVMVHVGGMVFSPGSVSVISDSLLTLPAIISIAAGGSLLL 1250
1251 IIVIIVLIAYKRKSRENDLTLKRLQMQMDNLESRVALECKEAFAELQTDI 1300
1301 NELTSDLDRSGIPYLDYRTYAMRVLFPGIEDHPVLRELEVQGNGQQHVEK 1350
1351 ALKLFAQLINNKVFLLTFIRTLELQRSFSMRDRGNVASLIMTGLQGRLEY 1400
1401 ATDVLKQLLSDLIDKNLENKNHPKLLLRRTESVAEKMLTNWFAFLLHKFL 1450
1451 KECAGEPLFMLYCAIKQQMEKGPIDAITGEARYSLSEDKLIRQQIEYKTL 1500
1501 ILNCVNPDNENSPEIPVKVLNCDTITQVKEKILDAVYKNVPYSQRPRAVD 1550
1551 MDLEWRQGRIARVVLQDEDITTKIEGDWKRLNTLMHYQVSDRSVVALVPK 1600
1601 QTSSYNIPASASISRTSISRYDSSFRYTGSPDSLRSRVPMITPDLESGVK 1650
1651 VWHLVKNHDHGDQKEGDRGSKMVSEIYLTRLLATKGTLQKFVDDLFETLF 1700
1701 STVHRGSALPLAIKYMFDFLDEQADRHSIHDTDVRHTWKSNCLPLRFWVN 1750
1751 VIKNPQFVFDIHKGSITDACLSVVAQTFMDSCSTSEHRLGKDSPSNKLLY 1800
1801 AKDIPSYKNWVERYYADIAKLPAISDQDMNAYLAEQSRLHATEFNMLSAL 1850
1851 NEIYSYVSKYSEELIGALEQDEQARRQRLAYKVEHLINAMSIES 1894
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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