 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q14767 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MRPRTKARSPGRALRNPWRGFLPLTLALFVGAGHAQRDPVGRYEPAGGDA 50
51 NRLRRPGGSYPAAAAAKVYSLFREQDAPVAGLQPVERAQPGWGSPRRPTE 100
101 AEARRPSRAQQSRRVQPPAQTRRSTPLGQQQPAPRTRAAPALPRLGTPQR 150
151 SGAAPPTPPRGRLTGRNVCGGQCCPGWTTANSTNHCIKPVCEPPCQNRGS 200
201 CSRPQLCVCRSGFRGARCEEVIPDEEFDPQNSRLAPRRWAERSPNLRRSS 250
251 AAGEGTLARAQPPAPQSPPAPQSPPAGTLSGLSQTHPSQQHVGLSRTVRL 300
301 HPTATASSQLSSNALPPGPGLEQRDGTQQAVPLEHPSSPWGLNLTEKIKK 350
351 IKIVFTPTICKQTCARGHCANSCERGDTTTLYSQGGHGHDPKSGFRIYFC 400
401 QIPCLNGGRCIGRDECWCPANSTGKFCHLPIPQPDREPPGRGSRPRALLE 450
451 APLKQSTFTLPLSNQLASVNPSLVKVHIHHPPEASVQIHQVAQVRGGVEE 500
501 ALVENSVETRPPPWLPASPGHSLWDSNNIPARSGEPPRPLPPAAPRPRGL 550
551 LGRCYLNTVNGQCANPLLELTTQEDCCGSVGAFWGVTLCAPCPPRPASPV 600
601 IENGQLECPQGYKRLNLTHCQDINECLTLGLCKDAECVNTRGSYLCTCRP 650
651 GLMLDPSRSRCVSDKAISMLQGLCYRSLGPGTCTLPLAQRITKQICCCSR 700
701 VGKAWGSECEKCPLPGTEAFREICPAGHGYTYASSDIRLSMRKAEEEELA 750
751 RPPREQGQRSSGALPGPAERQPLRVVTDTWLEAGTIPDKGDSQAGQVTTS 800
801 VTHAPAWVTGNATTPPMPEQGIAEIQEEQVTPSTDVLVTLSTPGIDRCAA 850
851 GATNVCGPGTCVNLPDGYRCVCSPGYQLHPSQAYCTDDNECLRDPCKGKG 900
901 RCINRVGSYSCFCYPGYTLATSGATQECQDINECEQPGVCSGGQCTNTEG 950
951 SYHCECDQGYIMVRKGHCQDINECRHPGTCPDGRCVNSPGSYTCLACEEG 1000
1001 YRGQSGSCVDVNECLTPGVCAHGKCTNLEGSFRCSCEQGYEVTSDEKGCQ 1050
1051 DVDECASRASCPTGLCLNTEGSFACSACENGYWVNEDGTACEDLDECAFP 1100
1101 GVCPSGVCTNTAGSFSCKDCDGGYRPSPLGDSCEDVDECEDPQSSCLGGE 1150
1151 CKNTVGSYQCLCPQGFQLANGTVCEDVNECMGEEHCAPHGECLNSHGSFF 1200
1201 CLCAPGFVSAEGGTSCQDVDECATTDPCVGGHCVNTEGSFNCLCETGFQP 1250
1251 SPESGECVDIDECEDYGDPVCGTWKCENSPGSYRCVLGCQPGFHMAPNGD 1300
1301 CIDIDECANDTMCGSHGFCDNTDGSFRCLCDQGFEISPSGWDCVDVNECE 1350
1351 LMLAVCGAALCENVEGSFLCLCASDLEEYDAQEGHCRPRGAGGQSMSEAP 1400
1401 TGDHAPAPTRMDCYSGQKGHAPCSSVLGRNTTQAECCCTQGASWGDACDL 1450
1451 CPSEDSAEFSEICPSGKGYIPVEGAWTFGQTMYTDADECVIFGPGLCPNG 1500
1501 RCLNTVPGYVCLCNPGFHYDASHKKCEDHDECQDLACENGECVNTEGSFH 1550
1551 CFCSPPLTLDLSQQRCMNSTSSTEDLPDHDIHMDICWKKVTNDVCSEPLR 1600
1601 GHRTTYTECCCQDGEAWSQQCALCPPRSSEVYAQLCNVARIEAEREAGVH 1650
1651 FRPGYEYGPGPDDLHYSIYGPDGAPFYNYLGPEDTVPEPAFPNTAGHSAD 1700
1701 RTPILESPLQPSELQPHYVASHPEPPAGFEGLQAEECGILNGCENGRCVR 1750
1751 VREGYTCDCFEGFQLDAAHMACVDVNECDDLNGPAVLCVHGYCENTEGSY 1800
1801 RCHCSPGYVAEAGPPHCTAKE 1821
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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