 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34241 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MSNHSEAYGSRDQRREKYTQGKEFEDGTLETLESIISAVEDETLSKDYQP 50
51 LIVFFQRGFGAQLVQTWSYYAQVNNHGKFSKTTSLLTKTLRVLSSDTSTV 100
101 TIGSGLIRLILTDYTKVLYRGLNNMRAQLTNPILRLLKQIVNFNNGQHIE 150
151 ELVSYFDFSLPILPRLLVPSKSELANGNSSADSSKHDSLRFTFIKFWLTL 200
201 ISNASPFVRKELLTENFKIMSNLFKFMNKADSDKLSEHILSVFINDILKE 250
251 KSFKRTTKTKILNELAASKIHHFYYSSNKNLVKKANEFFLTFGASRDFSV 300
301 AFPDNCVWFKNSVADGASHGAPITVNQVEFQIHNKLLFNTLRLFKPWEDT 350
351 LQLGTLIKILENVPELVAPYSIFLTTNGNYDPKMTSYWFGITLLINKIIN 400
401 LKIPQFMEKVDSNIPPATSLVIENILPSLLTKSSLVKSLQFETPIIRQLA 450
451 CQSIVLALKKLEKVSTFYDQKGWRNEKTILLNEFHTRIPDLPIFVSTLSN 500
501 SLASNKDNRILPLSISIIFNYYSKMFPNLFSINLPSSNIYTDIMQKSKIS 550
551 GIEFAILDNYLQFQEFNSTQTRWWNPSSGGNSLFTLLLKLASSKNASNVI 600
601 TTRISNLLDELTRTNVIFNISLISPVMALVNSLQGLSLQVSEIDNMEQVW 650
651 KWFDETISRVVKTPYKYVDMAKEYNYISPFIMCLSEQWKYVDKSGNPEFL 700
701 IKWLILFLRNMIFIGEDHIGIDKLVKNVFPEVSDHDVNIYLKLDSFEENI 750
751 KKTNSSNSLISSMKSSSFFQYISALPSKNLMNISRLPVNKLDAAGILFRV 800
801 QLLVEDDSVVYDNWFEATACELTGKIASYMVTDTEFPIIKVLERYINFAL 850
851 PKLAIEKRNALLMKKSRFMCNLIGAVCFETGHQLVEFREIIQKVVFSGEN 900
901 VEEYANYNELYQKEDVNAFLTSVSEYLSTSALTSLLMCSTKLESTRNILQ 950
951 KLFNEGKTIKISLVKNILNKAANEDPASIKEVNISLAKFFEENKVCVDAS 1000
1001 SDPMGKLSLSETTSLINSFVSSDLNYLVLKAFYRWEHFSFPSFIPSIWRI 1050
1051 KDSPLLSIVTTAALFKHMQDKDFSAFAHETISKYGNEIAKSTYTTSKSEI 1100
1101 FDEILNMITTYIDFYDETKRNEILKCVLSQSDHKYHAATVRYIAAHNNFT 1150
1151 YPGVETWLHKTLLYLTKYLSERKVISNSFFELLRAMAELLKLEEVPNKLN 1200
1201 VKIINSQLEAILGSEWIKQIKVLEYVIVLIFCVSKKSIQSQRMVQLLLSN 1250
1251 DSYSSIMIKDNDEDSSYRKFLSTMILFSLFSIDPVVNSTPIVQEKLLTFY 1300
1301 SGTISSNDKLILKILETIESHTATSWTNMIFSWEFIKDEEEEILEAIGDT 1350
1351 RLITKEREGLILTLQKNMIKKSIDRYVLERPQVPELYTDSNTNNYDATTR 1400
1401 CDLVKKYYDDTERSGVDMYDPLFLLLLIIHNKELVKMVKDDEGNVTYRYE 1450
1451 FENFLDCKIFQFIICSLSDCHTVANISYEHLSNLASSLEKKTAQMNLEKQ 1500
1501 ITSKDNERKESDSDLIKYNSIYQVLIKRILYQRQQNQDPINPLIWFSISR 1550
1551 IVDLLGSPTAPLHEKAYRWVLSNSTIRSWDIPMVSDVMMSYNKRQQDDNK 1600
1601 KEIDMEIYYGELSWVLTTICKGIKTDEDYKMLEKKGVFEWLLNLINMPYL 1650
1651 KERLRELIYFIFYKVQRVADDGGLNLISRNGIVSFFEVLNNNIKSRLPQD 1700
1701 DILNNIGTLRNENRGTLNTTLRLAQEQNGIEKLLLGYNELVKSQKRLILW 1750
1751 TEGDSDNVVKRLRK 1764
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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