 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9BYB0 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MQLSRAAAAAAAAPAEPPEPLSPAPAPAPAPPGPLPRSAADGAPAGGKGG 50
51 PGRRAESPGAPFPGASGPGPGPGAGMDGPGASAVVVRVGIPDLQQTKCLR 100
101 LDPAAPVWAAKQRVLCALNHSLQDALNYGLFQPPSRGRAGKFLDEERLLQ 150
151 EYPPNLDTPLPYLEFRYKRRVYAQNLIDDKQFAKLHTKANLKKFMDYVQL 200
201 HSTDKVARLLDKGLDPNFHDPDSGECPLSLAAQLDNATDLLKVLKNGGAH 250
251 LDFRTRDGLTAVHCATRQRNAAALTTLLDLGASPDYKDSRGLTPLYHSAL 300
301 GGGDALCCELLLHDHAQLGITDENGWQEIHQACRFGHVQHLEHLLFYGAD 350
351 MGAQNASGNTALHICALYNQESCARVLLFRGANRDVRNYNSQTAFQVAII 400
401 AGNFELAEVIKTHKDSDVVPFRETPSYAKRRRLAGPSGLASPRPLQRSAS 450
451 DINLKGEAQPAASPGPSLRSLPHQLLLQRLQEEKDRDRDADQESNISGPL 500
501 AGRAGQSKISPSGPGGPGPAPGPGPAPPAPPAPPPRGPKRKLYSAVPGRK 550
551 FIAVKAHSPQGEGEIPLHRGEAVKVLSIGEGGFWEGTVKGRTGWFPADCV 600
601 EEVQMRQHDTRPETREDRTKRLFRHYTVGSYDSLTSHSDYVIDDKVAVLQ 650
651 KRDHEGFGFVLRGAKAETPIEEFTPTPAFPALQYLESVDVEGVAWRAGLR 700
701 TGDFLIEVNGVNVVKVGHKQVVALIRQGGNRLVMKVVSVTRKPEEDGARR 750
751 RAPPPPKRAPSTTLTLRSKSMTAELEELASIRRRKGEKLDEMLAAAAEPT 800
801 LRPDIADADSRAATVKQRPTSRRITPAEISSLFERQGLPGPEKLPGSLRK 850
851 GIPRTKSVGEDEKLASLLEGRFPRSTSMQDPVREGRGIPPPPQTAPPPPP 900
901 APYYFDSGPPPAFSPPPPPGRAYDTVRSSFKPGLEARLGAGAAGLYEPGA 950
951 ALGPLPYPERQKRARSMIILQDSAPESGDAPRPPPAATPPERPKRRPRPP 1000
1001 GPDSPYANLGAFSASLFAPSKPQRRKSPLVKQLQVEDAQERAALAVGSPG 1050
1051 PGGGSFAREPSPTHRGPRPGGLDYGAGDGPGLAFGGPGPAKDRRLEERRR 1100
1101 STVFLSVGAIEGSAPGADLPSLQPSRSIDERLLGTGPTAGRDLLLPSPVS 1150
1151 ALKPLVSGPSLGPSGSTFIHPLTGKPLDPSSPLALALAARERALASQAPS 1200
1201 RSPTPVHSPDADRPGPLFVDVQARDPERGSLASPAFSPRSPAWIPVPARR 1250
1251 EAEKVPREERKSPEDKKSMILSVLDTSLQRPAGLIVVHATSNGQEPSRLG 1300
1301 GAEEERPGTPELAPAPMQSAAVAEPLPSPRAQPPGGTPADAGPGQGSSEE 1350
1351 EPELVFAVNLPPAQLSSSDEETREELARIGLVPPPEEFANGVLLATPLAG 1400
1401 PGPSPTTVPSPASGKPSSEPPPAPESAADSGVEEADTRSSSDPHLETTST 1450
1451 ISTVSSMSTLSSESGELTDTHTSFADGHTFLLEKPPVPPKPKLKSPLGKG 1500
1501 PVTFRDPLLKQSSDSELMAQQHHAASAGLASAAGPARPRYLFQRRSKLWG 1550
1551 DPVESRGLPGPEDDKPTVISELSSRLQQLNKDTRSLGEEPVGGLGSLLDP 1600
1601 AKKSPIAAARLFSSLGELSSISAQRSPGGPGGGASYSVRPSGRYPVARRA 1650
1651 PSPVKPASLERVEGLGAGAGGAGRPFGLTPPTILKSSSLSIPHEPKEVRF 1700
1701 VVRSVSARSRSPSPSPLPSPASGPGPGAPGPRRPFQQKPLQLWSKFDVGD 1750
1751 WLESIHLGEHRDRFEDHEIEGAHLPALTKDDFVELGVTRVGHRMNIERAL 1800
1801 RQLDGS 1806
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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