 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UTK4 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MFGQNNSSGFGGGTGAFGQNNQQTGGLFGSNSNTPGNTLFGSQNTSTTGF 50
51 GQNTTQPLFGSNTNGGLFGNRNNTTTTGGTGFGMSSGTGMFGQSNTPAFG 100
101 GTNNATNPSGGGLFGSNTANNNANTGTSFSFGSNAGSTGFGNTASNTGTG 150
151 GGLFGSQNNAGNTAGNTGFGSQGTGGGLFGSSTTPATTNAFGTSGFVSSN 200
201 ANAVNGTANPPYAVTSEKDPQTNGTSVFQSITCMPAYRSYSFEELRLQDY 250
251 NQGRRFGNASSTNTTSAFGSTPAFGASTTPFGQNLSGTTNNATPFGTSNA 300
301 TNTTPGSGLFGGGSAFGSNTTNTGFGSGTNNASGGLFGQNNNTTSTPSTG 350
351 LFGGSTFNQQKPAFSGFGSTTNTTNTGTGTGLFGSNNATNTGTGQTTGGL 400
401 FGGAATGTGTGFGSSTGGFGSNTNNQPNSGTMGTGLFGFGANNNTANNNT 450
451 APTSTFGGNNSSNFSFGANNNAATKPSGFGFGSTTTTPASGGFSFGQNAN 500
501 NAPKPAFGSTATTAPKPAGTGLFGGLGAGANTNTATNATGTGGSLFGNAN 550
551 TAGSNMFGSANSSTPGTGLFGSTQTNNATSNTGTGLFGSNNANTTNTGGS 600
601 LFNKPSTTTGGLFGNTTAQQPSTTTSGLFGASNTNNQAQTSNFGTGLFGG 650
651 SQAGQQQQPLQASIDQNPYGNNPLFSSTTSQVAPTSIQEPIASPLTSKPT 700
701 PKKAASLPQFWLSPRSHNTARLASISSFAKSAVMNSTSASGKPKSLHLFD 750
751 SLNDDVLLSADAFTPRQNIKKLVITHKISKDDILQNGVKNGNDAKSDSKV 800
801 QEKAPQNEADGSLKKDEHVVLSDDYWMKPSIEELSKYPKEKLCSVHQFSV 850
851 GRTGYGQVAFLKPVDLSGFEKLEDIPGKVVVFERKICAVYPVEGSSPPLG 900
901 EGLNVPAIITLEKTWPLSRETREPIKDPQNPRYIQHVKRLHRIKDTEFID 950
951 FNDGKWIFKVQHFSRYGLLDDEEEENDMSSTSNEAGNLKKYDQPNLKVSG 1000
1001 KNDSFVTHHTPGAFPNDSKNKELNRHFLKVDDSAPLDDTFMSKKVKLDFS 1050
1051 SDSNVSERGDYDDNAKKVDEVISIEKVDGYSKENNVPLSEDDLSNSSESS 1100
1101 NESVYSLVEESDASLAADNMDIEDISEESDREELSSMRFGAQDFHGLVVT 1150
1151 DNWRDQLNLSVQRSALIKAAFPESQSNANLKNSRGIYYNEHDLVTDIFGN 1200
1201 QNLDTDRPWQSLDKPGAFIPSKFHFTANGSCIYVLKSSDVKIRSIYDFIP 1250
1251 TKDPNGTKLLEYQLDQTEVYLDLSGTHAASPRSSMTVKPLSLCSSGYESI 1300
1301 VWDLTSILFDPKNYSLPSELSSEAREVLYQKLVRESLSEWITKTLEHETT 1350
1351 TLAKEAETSEERIYILLTGNLIGQACEEAVQSQNNRLSTLIPLVNSDVDI 1400
1401 QQEVKQQLEEWRKHGDLPFINKFTRLIFELLSGNTDIAEGCGTKGDEDYV 1450
1451 QSIPITKNMTWLRAFGLKLWYNTDISIGEAMQLYVESLQKFPEIMQKPIA 1500
1501 TSAVQGIEVYDIIYLLLKAYAMGTSLEELTIPESAKCSPLNYRVVWQLAI 1550
1551 YLSKARSLCDFSDRVVDINMAEDLKPISVHSDQLTLAYASQLEASGQWLW 1600
1601 SLFVLLHLENVETRTSTITSCLARNLRGGLGAGAVEMIEKLCIPESWLNE 1650
1651 AKALYARYVGDHLNELYFLQEAALYEDAHKVLLDTLAPQAVISGNKTQLK 1700
1701 KALEGFNGQTDGLASWRFGGQIYSDYLDLLEGNFDANQELKLFTLRKISV 1750
1751 ALKELNATNLLQKAALHKISRFVNALCNEESLTDAICNLPLPLADSLANL 1800
1801 QNISVQF 1807
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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