 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O35806 from www.uniprot.org...
The NucPred score for your sequence is 0.54 (see score help below)
1 MRAPTTVRCSGRIQRARWRGFLPLVLALLMGTSHAQRDSVGRYEPASRDA 50
51 NRLWRPVGNHPAAAAAKVYSLFREPDAPVPGLSPSEWNQPGQGIPGRLAE 100
101 AEARRPSRAQQLRRVQSPVQTRRSNPRGQQPPAARTAHSVVRLATPQRPA 150
151 AARRGRLTGRNVCGGQCCPGWTTSNSTNHCIKPVCQPPCQNRGSCSRPQL 200
201 CICRSGFRGARCEEVIPEEEFDPQNARPVPRRSVEGAPGPHRSSEARGSL 250
251 VTRIQPLLPPLPPPPSRTLSQTRPLQQHAGLSRTVRRYPATGTNGQLMSN 300
301 ALPSGPGPELRDSSQQAAHMNHLSHPWGLNLTEKIKKIKVVFTPTICKQT 350
351 CARGRCANTCEKGDTTTLYSQGGHGHDPKSGFRIYFCQIPCLNGGRCIGR 400
401 DECWCPANSTGKFCHLPVPQPDREPPGRGSQHRALLEGPLKQSTFTLPLS 450
451 NQLASVNPSLVKVQMQHPPEASVQIHQVARVRGEVDPVPEDNSVETRASH 500
501 RPHGSSGHSHWASNSIPARAGEAPRPPPVPSRHYGLLGQCYLSTVNGQCA 550
551 NPLGELTSQEDCCGSVGTSWGVTSCAPCPPRPAFPVIENGQLECPQGYKR 600
601 LNLSHCQDINECLTLGLCKDSECVNTRGSYLCTCRPGLMLDPSRSRCVSD 650
651 KAVSMKQGLCYRSMVSGTCTLPLVQRITKQICCCSRVGKAWGSKCEHCPL 700
701 PGTEAFREICPAGHGYAYSSSDIRLSMRKAEEEELASPVREQRQQSSGPP 750
751 PGAAERQPLRAATATWIEAETLPDKGDSRAIQITTSAPHLPARVPGDATG 800
801 RPTPSLPGQGIPEGPAEEQVIPSSDVLVTHGPPGFDPCFAGASNICGPGT 850
851 CVKLPNGYRCVCSPGYQLHPSQDYCTDDNECLRNPCEGRGRCVNSVGSYS 900
901 CLCYPGYTLATLGDTQECQDVDECEQPGVCSGGRCSNTEGSYHCECDQGY 950
951 VMVRRGHCQDINECRHPGTCPDGRCVNSPGSYTCLACEEGYIGQSGNCVD 1000
1001 MNECLTPGICAHGRCINMEGSFRCSCEPGYELTPDKKGCRDVDECASRAS 1050
1051 CPTGLCLNTEGSFTCSACQSGYWVNEDGTACEDLDECAFPGVCPTGVCTN 1100
1101 TVGSFSCKDCDRGFRPSPLGNSCEDVDECEGPQNSCLGGECKNTDGSYQC 1150
1151 LCPQGFQLANGTVCEDVDECVGEEHCAPHGECLNSPGSFFCLCAPGFASA 1200
1201 EGGTRCQDVDECATTEPCLGGHCVNTEGSFNCLCETGFQPAPDSGECVDI 1250
1251 DECANDTVCGNHGFCDNTDGSFRCLCDQGFETSPSGWECVDVNECELMLA 1300
1301 VCGDALCENVEGSFLCLCASDLEEYDAEEGHCRPRVAGAQRIPEVPTEEQ 1350
1351 AAGLTGMECYAEHNGGPPCSQILGQNSTQAECCSTQGARWGETCDPCPSE 1400
1401 DSVEFSELCPSGQGYIPVEGAWTFGQAMYTDADECILFGPALCQNGRCLN 1450
1451 TVPGYICLCNPGYHYDAVSRKCQDHNECQDLACENGECVNTEGSFHCFCS 1500
1501 PPLILDLSGQRCVNSTSSSEDFPDHDIHMDICWKKVTNDVCSQPLRGHHT 1550
1551 TYTECCCQDGEAWSQQCALCPPRSSEVYAQLCNVARIEAEREAGIHFRPG 1600
1601 YEYGPGPDDLPETLYGPDGAPFYNYLGPEDTVPEPPFSNTASHLGDNTPI 1650
1651 LEPPLQPSELQPPAIQNPLASFEGLQAEECGILNGCENGRCVRVREGYTC 1700
1701 DCFEGFQLDTALMACVDVNECEDLNGAARLCAHGHCENTEGSYRCHCSPG 1750
1751 YVAEPGPPHCAAKE 1764
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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