 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UIW2 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MPLPPRSLQVLLLLLLLLLLLPGMWAEAGLPRAGGGSQPPFRTFSASDWG 50
51 LTHLVVHEQTGEVYVGAVNRIYKLSGNLTLLRAHVTGPVEDNEKCYPPPS 100
101 VQSCPHGLGSTDNVNKLLLLDYAANRLLACGSASQGICQFLRLDDLFKLG 150
151 EPHHRKEHYLSSVQEAGSMAGVLIAGPPGQGQAKLFVGTPIDGKSEYFPT 200
201 LSSRRLMANEEDADMFGFVYQDEFVSSQLKIPSDTLSKFPAFDIYYVYSF 250
251 RSEQFVYYLTLQLDTQLTSPDAAGEHFFTSKIVRLCVDDPKFYSYVEFPI 300
301 GCEQAGVEYRLVQDAYLSRPGRALAHQLGLAEDEDVLFTVFAQGQKNRVK 350
351 PPKESALCLFTLRAIKEKIKERIQSCYRGEGKLSLPWLLNKELGCINSPL 400
401 QIDDDFCGQDFNQPLGGTVTIEGTPLFVDKDDGLTAVAAYDYRGRTVVFA 450
451 GTRSGRIRKILVDLSNPGGRPALAYESVVAQEGSPILRDLVLSPNHQYLY 500
501 AMTEKQVTRVPVESCVQYTSCELCLGSRDPHCGWCVLHSICSRRDACERA 550
551 DEPQRFAADLLQCVQLTVQPRNVSVTMSQVPLVLQAWNVPDLSAGVNCSF 600
601 EDFTESESVLEDGRIHCRSPSAREVAPITRGQGDQRVVKLYLKSKETGKK 650
651 FASVDFVFYNCSVHQSCLSCVNGSFPCHWCKYRHVCTHNVADCAFLEGRV 700
701 NVSEDCPQILPSTQIYVPVGVVKPITLAARNLPQPQSGQRGYECLFHIPG 750
751 SPARVTALRFNSSSLQCQNSSYSYEGNDVSDLPVNLSVVWNGNFVIDNPQ 800
801 NIQAHLYKCPALRESCGLCLKADPRFECGWCVAERRCSLRHHCAADTPAS 850
851 WMHARHGSSRCTDPKILKLSPETGPRQGGTRLTITGENLGLRFEDVRLGV 900
901 RVGKVLCSPVESEYISAEQIVCEIGDASSVRAHDALVEVCVRDCSPHYRA 950
951 LSPKRFTFVTPTFYRVSPSRGPLSGGTWIGIEGSHLNAGSDVAVSVGGRP 1000
1001 CSFSWRNSREIRCLTPPGQSPGSAPIIININRAQLTNPEVKYNYTEDPTI 1050
1051 LRIDPEWSINSGGTLLTVTGTNLATVREPRIRAKYGGIERENGCLVYNDT 1100
1101 TMVCRAPSVANPVRSPPELGERPDELGFVMDNVRSLLVLNSTSFLYYPDP 1150
1151 VLEPLSPTGLLELKPSSPLILKGRNLLPPAPGNSRLNYTVLIGSTPCTLT 1200
1201 VSETQLLCEAPNLTGQHKVTVRAGGFEFSPGTLQVYSDSLLTLPAIVGIG 1250
1251 GGGGLLLLVIVAVLIAYKRKSRDADRTLKRLQLQMDNLESRVALECKEAF 1300
1301 AELQTDIHELTNDLDGAGIPFLDYRTYAMRVLFPGIEDHPVLKEMEVQAN 1350
1351 VEKSLTLFGQLLTKKHFLLTFIRTLEAQRSFSMRDRGNVASLIMTALQGE 1400
1401 MEYATGVLKQLLSDLIEKNLESKNHPKLLLRRTESVAEKMLTNWFTFLLY 1450
1451 KFLKECAGEPLFMLYCAIKQQMEKGPIDAITGEARYSLSEDKLIRQQIDY 1500
1501 KTLTLNCVNPENENAPEVPVKGLDCDTVTQAKEKLLDAAYKGVPYSQRPK 1550
1551 AADMDLEWRQGRMARIILQDEDVTTKIDNDWKRLNTLAHYQVTDGSSVAL 1600
1601 VPKQTSAYNISNSSTFTKSLSRYESMLRTASSPDSLRSRTPMITPDLESG 1650
1651 TKLWHLVKNHDHLDQREGDRGSKMVSEIYLTRLLATKGTLQKFVDDLFET 1700
1701 IFSTAHRGSALPLAIKYMFDFLDEQADKHQIHDADVRHTWKSNCLPLRFW 1750
1751 VNVIKNPQFVFDIHKNSITDACLSVVAQTFMDSCSTSEHKLGKDSPSNKL 1800
1801 LYAKDIPNYKSWVERYYADIAKMPAISDQDMSAYLAEQSRLHLSQFNSMS 1850
1851 ALHEIYSYITKYKDEILAALEKDEQARRQRLRSKLEQVVDTMALSS 1896
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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