 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P41809 from www.uniprot.org...
The NucPred score for your sequence is 0.56 (see score help below)
1 MVSLKIKKILLLVSLLNAIEAYSNDTIYSTSYNNGIESTPSYSTSAISST 50
51 GSSNKENAITSSSETTTMAGQYGESGSTTIMDEQETGTSSQYISVTTTTQ 100
101 TSDTMSSVKKSTEIATPSSSIVPTPLQSYSDESQISQTLSHNPKSVAESD 150
151 SDTTSSESSSSVIISTSDSSAVPREISPIITTDSQISKEEGTLAQTSSIS 200
201 ETTRIAQMVTRVSQISSITAASTIDGFSSESTQTDFSNTVSFENSVEEEY 250
251 AMSKSQLSESYSSSSTVYSGGESTADKTSSSPITSFSSSYSQTTSTETSE 300
301 SSRVAVGVSRPSSITQTTSIDSFSMSEVELSTYYDLSAGNYPDQELIVDR 350
351 PATSSTAETSSEASQGVSRESNTFAVSSISTTNFIVSSASDTVVSTSSTN 400
401 TVPYSSVHSTFVHATSSSTYISSSLYSSPSLSASVSSHFGVAPFPSAYIS 450
451 FSSVPVAVSSTYTSSPSASVVVPSAYASSPSVPVAVSSTYTSSPSAPAAI 500
501 SSTYTSSPSAPVAVSSTYTSSPSAPAAISSTYTSSPSAPVAVSSTYTSSP 550
551 SAPAAISSTYTSSPSAPVAVSSTYTSSPSAPVAISSTYTSSPSVPVAVSS 600
601 TYTSSPSAPAAISSTYTSSPSAPVAVSSTYTSSPSAPAAISSTYTSSPSV 650
651 PVAVSSTYTSSPSAPAAISSTYTSSPSVPVAVSSTYTSSPSAPAAISSTY 700
701 TSSPSAPVAVSSTYTSSPSAPAAISSTYTSSPSAPVAVSSTYTSSPSAPA 750
751 AISSTYTSSPSAPVAVSSTYTSSPSALVVLSSTSTSSPYDIVYSPSTFAA 800
801 ISSGYTPSPSASVAMSSTSSSSPYDIVYSLSSSASRSSIATYEFSPSPST 850
851 SLPTSSTYTYFSSAYAFEFSSERYSTTSTIAPTQIHSTLSRITDFLLQTS 900
901 MAIQSIVSQQISTSSTLNDEIHSSALSVFNPSASNLVETSLIISSTQASI 950
951 TSPKNSAKISSLQSQLSSSTKNPYDTANKNTETSGRSTVVSNFLYTSSAA 1000
1001 KPDNEKFSATPTEITTISSSSHAYSLSIPSSHNSVTGLSHNFVDSSKSAT 1050
1051 SFGYSSSSISSIKLSKETIPASKSVSNTQERITSFTSTLRANSQSEKSEG 1100
1101 RNSVGSLQSSHISSNPSLSTNTKVDSKSLSRKVSKTMGENGEETGLTTTK 1150
1151 TQYKSSSETSGSYSRSFTKISIGPATTAVQTQASTNSVFTAPALSTYPTT 1200
1201 PYPSPNSYAWLPTAIIVESSETGPTTASFNPSITGSLPNAIEPAVAVSEP 1250
1251 INHTLITIGFTAALNYVFLVQNPLSSAQIFNFLPLVLKYPFSNTSSELDN 1300
1301 SIGELSTFILSYRSGSSTTTLSPKSISSLSVVKKKKNQQKKNATKSTEDL 1350
1351 HPPQVDTSSIAVKKIVPMVDSSKAYIVSVAEVYFPTEAVTYLQQLILDEN 1400
1401 STLYSNPQTPLRSLAGLIDSGIPLGGLTLYGSGDGGYVPSLTSSSVLDSS 1450
1451 KGNSQNIDGTYKYGALDDFINSFTDSASAGKYAVKIIIFLIVLTIGVLLW 1500
1501 LFVAFFAFRHRNILLKRHPRNCIGKSLNNERELESTELSRSSSGNQVYNE 1550
1551 KPPESENESVYSAVDDHYIVTGENTVYNTIHRLHYTINDDGDLLYRDAIP 1600
1601 LDFDQTNGDDGSGIDSIVRDCVYDKNQDATEAFLNDEESISGILDVDENG 1650
1651 DIRLYDSYSDNEESNSFHLPDEVIENYNKNHLCETKLHGLGTESCTTDDP 1700
1701 DTGNQITNEFSTGSQTCLPSTAYTTPLHTNSIKLHTLRYTESSLPKPNQT 1750
1751 LFSNLEDLEIEDIDDNGSVSDVHIEELDALDEELYKRMSKVIKQQNHQTT 1800
1801 KI 1802
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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