 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q28019 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MRPPTTARCPGRVLQNPWRSFWPLTLALFVGMGQAQRDPVGRYEPAGRDA 50
51 SRLRRPGGSPVVATAKVYSLFREQDAPVRGSPPAELVQPSWGSPRRSTEA 100
101 EARRPPRAQQPRRVQPPAQTWRSRPSGQQQSAPRARAAPALPRLETVQRP 150
151 RAARGRLTGRNVCGGQCCPGWTTANSTNHCIKPVCQPPCQNRGSCSRPQL 200
201 CVCRSGFRGARCEEVIPEEEFDPQNSRPAPRRSAEGPPSLRRSSVAREST 250
251 TARVRPPAPQLQRARTLSGLSQTRSSQQHVGLSQTTRLYPAPAASGQLTS 300
301 NALPMGPGPERRDGAPQAAYLDRPSSSWGLNLTEKIKKIKIVFTPTICKQ 350
351 TCARGRCANSCERGDTTTLYSQSGHGHDPKSGFRIYFCQIPCLNGGRCIG 400
401 RDECWCPANSTGKFCHLPAPRLDQEPPERGPRHRAPLEGPLKQSTFTLPL 450
451 SNQLASVNPSLVNVHIRHPPEASVQIHQVARVRGEAEEAPEENSVETRPS 500
501 PRLPAGPGPGRWDSNRIPARSGEAPRLPPPVVPRTPALLGRCYLSTLNGQ 550
551 CANPLPELTAHEDCCGSVGAFWGVTSCAPCPPRPASPVIENGQLECPQGY 600
601 KRLNLTHCEDVNECLTLGLCEDSECVNTRGSYLCTCRPGLLLDPSRSRCV 650
651 SDKAVSMQQGLCYRLLGPGTCALPLAQRITKQICCCSRVGKAWGSLCEKC 700
701 PLPGTEAFREICPAGHGYTYSSSDIRLSMRKAEEEELARPSRDRGPKRNG 750
751 TLPRPAERQPLRAATGTWVEAETIPDKGDSQASQVTTSVTQLSTWVPGGA 800
801 LGTPTPSVPEQGIPEAREEAQVTAPTNVLVTPAPSGIDRCAAGATNICGP 850
851 GTCVNLPDGYRCICSPGYRLHPSQAYCTDDNECLRDPCKGRGRCVNRVGS 900
901 YSCFCYPGYKLATSGATQECQDIDECEQPGVCSRGRCTNTEGSYHCECDQ 950
951 GYIMVRKGHCQDINECRHPGTCPDGKCVNSPGSYTCLPCEEGYRGQGGSC 1000
1001 VDVNECLTPGVCTHGTCINLEGSFRCSCEQGYEVTPDEKGCKDVDECAIR 1050
1051 ASCPTGLCLNTEGSFTCSACESGYWVNEDGTACEDLDECAFPGVCPSGVC 1100
1101 TNTAGSFSCRDCEAGYQPSALGHTCEDVDECEDPQSSCLGGECKNTAGSY 1150
1151 QCLCPPGFQLANGTVCEDVDECVGEEYCAPRGECLNSHGSFFCLCADGFV 1200
1201 SADGGTSCQDVDECAVTDRCVGGQCVNTDGSFNCVCETGFQPSPESGECV 1250
1251 DIDECEDLGEPICGAWRCENSPGSYRCVLGCQPGFHMAPTGDCIDIDECA 1300
1301 NDTVCGSHGFCDNTDGAFRCLCDQGFETSPSGWDCVDVNECELMLAVCGA 1350
1351 ALCENVEGSFLCLCASDLEEYDAQEGRCRPRGAGGPSVPEARPGAHPPGP 1400
1401 VRMECYSGQKDQTPCSSLLGRNTTQAECCCTQGTGWGDACDLCPDEDSVE 1450
1451 FSEICPSGKGYIPVEGAWMFGQTTYTDADECVMFGPGLCRNGRCLNTVPG 1500
1501 YICLCNPGYHYNAASRKCEDHDECQDMACENGECVNTEGSFHCFCSPPLT 1550
1551 LDLSQQRCVNSTSGVEDLPDHDIHMDICWKRVTNYVCSHPLHGRRTTYTE 1600
1601 CCCQDGEAWSQQCALCPPRSSEVYAQLCNVARIEAEREAGIHFRPGYEYS 1650
1651 PGPDDLHYSLYGPDGVPFYNYLGPEDAIPEPLFPSTAGRPGDRIPLPEPP 1700
1701 LQPSELQPHYVASHPGLCCCKNGEEVGSEAVSLPSSPSAHHCQQEHQAGF 1750
1751 EGLQAEECGILNGCENGRCVRVREGYTCDCFEGFQLDTAHMACVDVNECD 1800
1801 DLNGPAALCVHGHCDNTEGSYRCHCLPGYVAEAGPPHCTAKE 1842
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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