 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JIR0 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MEQLTTLPRLGDPGAMEPWALPAWQHWTQGQGCKPGDASASIAATPTALQ 50
51 VKGLRFEESSEPAGAHSPGPIRNTDPEGTETVLPKLGQQAESPGYSCSRL 100
101 EGEDAQAYKAKFNIGFGDRPNLELLRALGELQQHCTILKEENQMLRKSSF 150
151 PETEEKVRRLKRKNAELAVIAKRLEERAQKLQETNMRGGEVPLCPDPDPV 200
201 WSCARKALARQRARDLSETATALLAKDKQNAALQRECRELQARLSLVGKE 250
251 GPQWLHMRDFDRLLRESQREVLRLQRQIALRNQREPLRPARSQGSTAPSS 300
301 VGAPAPGAPGETVLEDDVESPQVVLGEPEKQLRVQQLESELCKKRKKCES 350
351 LEQEARKKQRRCEELELQLRAAQNENARLVEENSRLSGKATEKEQVEWEN 400
401 AELKGQLLGVTQERDSALRKSQGLQSKLESLEQVLEHMRKVAQRRQQLEE 450
451 EHEQARLSLQEKQEEVRRLQQAQAEAKREHEGAVQLLESTLDSMQARVRE 500
501 LEGQCRSQTERFSLLAQELQAFRLHPGPLDLLTSALGCNALGDHPPPHCC 550
551 CSSPQPCQGSGPKDLDLPPGSPGRCTPKSSEPALATLTGVPRRTAKKAES 600
601 LSNSSRSESIHNSPKSCPTPEVDTASEVEELEVDSVSLLPAAPEGHSGGG 650
651 ARIQVFLARYSYNPFEGPNENPEAELPLTAGEYIYIYGNMDEDGFFEGEL 700
701 MDGRRGLVPSNFVERVSDDDLLSTLPRELADSSHSSGPELSFLSGGGGGC 750
751 SSGGQSSGGRSQPRPEEEATGDELSLSPPPEGLGEPLAVPYPRHITVLKQ 800
801 LAHSVVLAWELPPERVDLRGFHIFVNGELRQALGPGVPPKAVLENMDLRA 850
851 GPLHVSVQALTSKGSSDPLRCCLAMGAGAGVVPSQLRIHRLTATSAEITW 900
901 VPGNSNLAHAVYLNGEECPPARPSTYWATFCNLRPGTLYQARVEAQIPSQ 950
951 GPWEPGWERPELRAATLQFTTLPAGLPDAPLDVQAEPGPSPGIVMISWLP 1000
1001 VTIDAAGTSNGVRVTGYAVYADGQKIMEVASPTAGSVLVEVSQLQLLQAC 1050
1051 HEVTVRTMSPHGESTDSIPAPVAPALASACQPARMSCLSPRPSPEVRTPL 1100
1101 ASVSPGLGYTSLPLRHPVPHGTQDSPASLSTEMSKGPQEEPPVPCSQEEA 1150
1151 GSAVHRTSEEKRAMEPTLGQEGPDPVAPFLAKQAVECTSGDAGPTPCSTQ 1200
1201 EELTQKEPSTEVCHRGDLDSELKLRSEKEGMSELGVHLVNSLVDHSRNSD 1250
1251 LSDIQEEEEEEEEEEELGSRPWSFQKQVAGNSIGENGAKPQPDPSCETDS 1300
1301 DEEILEQILELPLQRLCSKKLFSIPEEEEEEDEEEGLGKPGPSSSSQDPS 1350
1351 QPERALLGLDCESSQPQGPGLCPLSPELSGAREHLEDVLGVVGGNSRRRG 1400
1401 GCSPEKLPNRKRPQDPREHCSRLLGNGGPQTSARPVPPRDRGSLPVIEGT 1450
1451 RVGQEPGGRGRPGLSRRCPRGPAPESSLVSCLSPKCLEISIEYDSEDEQE 1500
1501 VGSGGVSISSSCYPTDGEAWGTAAVGRPRGPVKVNSGSNTYLRLPAWEKG 1550
1551 EPERRGRSAIGRTKEPPSRATETGESRGQDNSGRRGPQRRGARVLRTGTT 1600
1601 ELAPPRSPQEAPSHQDLPLRVFVALFDYDPISMSPNPDAGEEELPFREGQ 1650
1651 ILKVFGDKDADGFYRGESGGRTGYIPCNMVAEVAVDTPTGRQQLLQRGFL 1700
1701 PPNVLTQGSGNGPSVYPSAHTPGPPPKPRRSKKVELEDPAQLCPGPPKLI 1750
1751 HSAALKTSRPMVAAFDYNPRENSPNMDVEAELPFRAGDVITVFGNMDDDG 1800
1801 FYYGELNGQRGLVPSNFLEGPGPEAGGLDSGTSQAESQRTRRRRVQC 1847
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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