 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O08999 from www.uniprot.org...
The NucPred score for your sequence is 0.54 (see score help below)
1 MRAPTTARCSGCIQRVRWRGFLPLVLAVLMGTSHAQRDSIGRYEPASRDA 50
51 NRLWHPVGSHPAAAAAKVYSLFREPDAPVPGLSPSEWNQPAQGNPGWLAE 100
101 AEARRPPRTQQLRRVQPPVQTRRSHPRGQQQIAARAAPSVARLETPQRPA 150
151 AARRGRLTGRNVCGGQCCPGWTTSNSTNHCIKPVCQPPCQNRGSCSRPQV 200
201 CICRSGFRGARCEEVIPEEEFDPQNARPVPRRSVERAPGPHRSSEARGSL 250
251 VTRIQPLVPPPSPPPSRRLSQPWPLQQHSGPSRTVRRYPATGANGQLMSN 300
301 ALPSGLELRDSSPQAAHVNHLSPPWGLNLTEKIKKIKVVFTPTICKQTCA 350
351 RGRCANSCEKGDTTTLYSQGGHGHDPKSGFRIYFCQIPCLNGGRCIGRDE 400
401 CWCPANSTGKFCHLPVPQPDREPAGRGSRHRTLLEGPLKQSTFTLPLSNQ 450
451 LASVNPSLVKVQIHHPPEASVQIHQVARVRGELDPVLEDNSVETRASHRP 500
501 HGNLGHSPWASNSIPARAGEAPRPPPVLSRHYGLLGQCYLSTVNGQCANP 550
551 LGSLTSQEDCCGSVGTFWGVTSCAPCPPRQEGPAFPVIENGQLECPQGYK 600
601 RLNLSHCQDINECLTLGLCKDSECVNTRGSYLCTCRPGLMLDPSRSRCVS 650
651 DKAVSMQQGLCYRSLGSGTCTLPLVHRITKQICCCSRVGKAWGSTCEQCP 700
701 LPGTEAFREICPAGHGYTYSSSDIRLSMRKAEEEELASPLREQTEQSTAP 750
751 PPGQAERQPLRAATATWIEAETLPDKGDSRAVQITTSAPHLPARVPGDAT 800
801 GRPAPSLPGQGIPESPAEEQVIPSSDVLVTHSPPDFDPCFAGASNICGPG 850
851 TCVSLPNGYRCVCSPGYQLHPSQDYCTDDNECMRNPCEGRGRCVNSVGSY 900
901 SCLCYPGYTLVTLGDTQECQDIDECEQPGVCSGGRCSNTEGSYHCECDRG 950
951 YIMVRKGHCQDINECRHPGTCPDGRCVNSPGSYTCLACEEGYVGQSGSCV 1000
1001 DVNECLTPGICTHGRCINMEGSFRCSCEPGYEVTPDKKGCRDVDECASRA 1050
1051 SCPTGLCLNTEGSFTCSACQSGYWVNEDGTACEDLDECAFPGVCPTGVCT 1100
1101 NTVGSFSCKDCDQGYRPNPLGNRCEDVDECEGPQSSCRGGECKNTEGSYQ 1150
1151 CLCHQGFQLVNGTMCEDVNECVGEEHCAPHGECLNSLGSFFCLCAPGFAS 1200
1201 AEGGTRCQDVDECAATDPCPGGHCVNTEGSFSCLCETASFQPSPDSGECL 1250
1251 DIDECEDREDPVCGAWRCENSPGSYRCILDCQPGFYVAPNGDCIDIDECA 1300
1301 NDTVCGNHGFCDNTDGSFRCLCDQGFETSPSGWECVDVNECELMMAVCGD 1350
1351 ALCENVEGSFLCLCASDLEEYDAEEGHCRPRVAGAQRIPEVRTEDQAPSL 1400
1401 IRMECYSEHNGGPPCSQILGQNSTQAECCCTQGARWGKACAPCPSEDSVE 1450
1451 FSQLCPSGQGYIPVEGAWTFGQTMYTDADECVLFGPALCQNGRCSNIVPG 1500
1501 YICLCNPGYHYDASSRKCQDHNECQDLACENGECVNQEGSFHCLCNPPLT 1550
1551 LDLSGQRCVNTTSSTEDFPDHDIHMDICWKKVTNDVCSQPLRGHHTTYTE 1600
1601 CCCQDGEAWSQQCALCPPRSSEVYAQLCNVARIEAERGAGIHFRPGYEYG 1650
1651 PGLDDLPENLYGPDGAPFYNYLGPEDTAPEPPFSNPASQPGDNTPVLEPP 1700
1701 LQPSELQPHYLASHSEPPASFEGLQAEECGILNGCENGRCVRVREGYTCD 1750
1751 CFEGFQLDAPTLACVDVNECEDLNGPARLCAHGHCENTEGSYRCHCSPGY 1800
1801 VAEPGPPHCAAKE 1813
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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