 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P32634 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MTVLQPPSSVCYPLNLPIVPNPNLDEATRKKLTLECRTGAAVELARSGVF 50
51 VHGGLTLPLNLTIINSLQLQKELILYFGKQKDRNADFKTLADWISPEIFF 100
101 LDLISRTWQRINTTIDTTSENELNNGLSFKERLFHSMCFTESNIYIFGGL 150
151 MVSPHNGYELIATNELWKLDLKTKCWSLISENPQITRRFNHSMHVLNENN 200
201 ENQDTKLIIVGGLDNMDIPVKKIDIFNLRTSLWESESKSDENPASKGSSK 250
251 ILVNIDGMPISLSHDSNFSVLIENNQAEIPTLALYYPQREANTSRRGTDD 300
301 GSFSTYAHDLDDKSKLPKHHHHHHGDLKYFESDDADENAVKTLMSPIVIL 350
351 PLLGNSQGARMTSNPTQNNKENSILQVPFHLQYPSGNYFNYNIVVIGFYP 400
401 DPQPSNLHCFIYNIASGKWIRVNIACTECSISMHRFWKLLIWKSHHQALL 450
451 LGTRTDDFCSPSVQKFDHILSFSLPMLNGYNKLVNTKHTRTNNGIANSHN 500
501 LNVNLSLYDHLPYSNSSTIEHTNPYTVTQGYSLDDSGIPRLTSTATSQFE 550
551 NYSRYITVPLEMESTSSIFPPYAMVLGKDALEIFGKTLSDFEFITADGDS 600
601 IGVPVYLLRKRWGRYFDSLLSNGYANTSFNYEFNGDTSNIISFSPHTASK 650
651 TTKFGNSSQSSNGSLEKYFSKNGNSKSNSNTSLKKPHSVDFTSSTSSPKQ 700
701 RAISHNKLSPSEPILCADEEDSRSNTLKQHATGDTGLKETGTSNKRPIST 750
751 TCSSTGMVFRVPFQDMKNSKLGLSEQSGRSTRASSVSPPPVYKKSTNDGN 800
801 DSNCTLSNTPLVYRRASTVGTTTNSSVDDGFSSIRRASHPLQSYIIAKSS 850
851 PSSISKASPAEKAFSRRKSSALRFIASPNQSRQTSFASTASTASVVSSTS 900
901 GRRRNSNQISHLGSSASLPNSPILPVLNIPLPPQEKIPLEPLPPVPKAPS 950
951 RRSSSLAEYVQFGRDSPVASRRSSHSTRKSSSSDARRISNSSLLRNTLDS 1000
1001 QLLSNSYGSDIPYEASIQEYGMNNGRDEEEDGDNQDYGCISPSNIRPIFS 1050
1051 TINAININGNFKEGEFFSSKSYINNEKSRRLSYISNPESVESTNSNNNAI 1100
1101 IELEPLLTPRSLYMPWSTASVRAFAEFFYTAQINGKWLLAPVTLDLLIMA 1150
1151 KIYEIPILYELITEVLYKIISKKEEGLSVTCEALLNLFQQKVSRYCNENE 1200
1201 GKIRKQLDSSESYQDTLEIKRSLANIDNGYVDSYLLRNTSMAQSIHYTDD 1250
1251 SNGETEIDMHHTGISSIGSLANRAVPTVFAGGPRDSHNSIGSIAFPSNSG 1300
1301 VQNIRRSVSLFSPATKKKSSLSRETDPLDTSDQFTDDVPDSGPVSRQQNF 1350
1351 PRRSSSFTETVPTEPTRYNYQNLDSSKSNRASDDKEEQNEQATLQDISNF 1400
1401 DKYKVETLQKRNSNDGKDLDRTNDPLKNRGTEIPQNSSNLETDPFIRDSF 1450
1451 DSDSGSSFRSDSDDLDSQLGILPFTKMNKKLQEQTSQEFDDSIDPLYKIG 1500
1501 SSTPGSSRLHGSFSKYIRPNSQREDGSEYVNISSLENMVSPNALPPVDYV 1550
1551 MKSIYRTSVLVNDSNLMTRTKEAIELSKVLKKLKKKVLQDISQMDDEMRE 1600
1601 TGKPIFARGSSSPTLSRQHSDVATPLKQQENTRPALKFASSSPISEGFRK 1650
1651 SSIKFSQAPSTQISPRTSVTDFTASQQRRQHMNKRFSTQTTHSTSALFMN 1700
1701 PAFMPSAVNTGRKESEGHCEDRSATANRTNRKEDATTNDNDNIAPFPFFG 1750
1751 KRR 1753
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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