 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P2E3 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MEERRPHLDARPRNSHTNHRGPVDGELPPRARNQANNPPANALRGGASHP 50
51 GRHPRANNHPAAYWQREERFRAMGRNPHQGRRNQEGHASDEARDQRHDQE 100
101 NDTRWRNGNQDCRNRRPPWSNDNFQQWRTPHQKPTEQPQQAKKLGYKFLE 150
151 SLLQKDPSEVVITLATSLGLKELLSHSSMKSNFLELICQVLRKACSSKMD 200
201 RQSVLHVLGILKNSKFLKVCLPAYVVGMITEPIPDIRNQYPEHISNIISL 250
251 LQDLVSVFPASSVQETSMLVSLLPTSLNALRASGVDIEEETEKNLEKVQT 300
301 IIEHLQEKRREGTLRVDTYTLVQPEAEDHVESYRTMPIYPTYNEVHLDER 350
351 PFLRPNIISGKYDSTAIYLDTHFRLLREDFVRPLREGILELLQSFEDQGL 400
401 RKRKFDDIRIYFDTRIITPMCSSSGIVYKVQFDTKPLKFVRWQNSKRLLY 450
451 GSLVCMSKDNFETFLFATVSNREQEDLCRGIVQLCFNEQSQQLLAEVQPS 500
501 DSFLMVETTAYFEAYRHVLEGLQEVQEEDVPFQRNIVECNSHVKEPRYLL 550
551 MGGRYDFTPLIENPSATGEFLRNVEGLRHPRINVLDPGQWPSKEALKLDD 600
601 SQMEALQFALTRELAIIQGPPGTGKTYVGLKIVQALLTNESVWQISLQKF 650
651 PILVVCYTNHALDQFLEGIYNCQKTSIVRVGGRSNSEILKQFTLRELRNK 700
701 REFRRNLPMHLRRAYMSIMTQMKESEQELHEGAKTLECTMRGVLREQYLQ 750
751 KYISPQHWESLMNGPVQDSEWICFQHWKHSMMLEWLGLGVGSFTQSVSPA 800
801 GPENTAQAEGDEEEEGEEESSLIEIAEEADLIQADRVIEEEEVVRPQRRK 850
851 KEESGADQELAKMLLAMRLDHCGTGTAAGQEQATGEWQTQRNQKKKMKKR 900
901 VKDELRKLNTMTAAEANEIEDVWQLDLSSRWQLYRLWLQLYQADTRRKIL 950
951 SYERQYRTSAERMAELRLQEDLHILKDAQVVGMTTTGAAKYRQILQKVEP 1000
1001 RIVIVEEAAEVLEAHTIATLSKACQHLILIGDHQQLRPSANVYDLAKNFN 1050
1051 LEVSLFERLVKVNIPFVRLNYQHRMCPEIARLLTPHIYQDLENHPSVLKY 1100
1101 EKIKGVSSNLFFVEHNFPEQEIQEGKSHQNQHEAHFVVELCKYFLCQEYL 1150
1151 PSQITILTTYTGQLFCLRKLMPAKTFAGVRVHVVDKYQGEENDIILLSLV 1200
1201 RSNQEGKVGFLQISNRICVALSRAKKGMYCIGNMQMLAKVPLWSKIIHTL 1250
1251 RENNQIGPMLRLCCQNHPETHTLVSKASDFQKVPEGGCSLPCEFRLGCGH 1300
1301 VCTRACHPYDSSHKEFQCMKPCQKVICQEGHRCPLVCFQECQPCQVKVPK 1350
1351 TIPRCGHEQMVPCSVPESDFCCQEPCSKSLRCGHRCSHPCGEDCVQLCSE 1400
1401 MVTIKLKCGHSQPVKCGHVEGLLYGGLLVKCTTKCGTILDCGHPCPGSCH 1450
1451 SCFEGRFHERCQQPCKRLLICSHKCQEPCIGECPPCQRTCQNRCVHSQCK 1500
1501 KKCGELCSPCVEPCVWRCQHYQCTKLCSEPCNRPPCYVPCTKLLVCGHPC 1550
1551 IGLCGEPCPKKCRICHMDEVTQIFFGFEDEPDARFVQLEDCSHIFEVQAL 1600
1601 DRYMNEQKDDEVAIRLKVCPICQVPIRKNLRYGTSIKQRLEEIEIIKEKI 1650
1651 QGSAGEIATSQERLKALLERKSLLHQLLPEDFLMLKEKLAQKNLSVKDLG 1700
1701 LVENYISFYDHLASLWDSLKKMHVLEEKRVRTRLEQVHEWLAKKRLSFTS 1750
1751 QELSDLRSEIQRLTYLVNLLTRYKIAEKKVKDSIAVEVYSVQNILEKTCK 1800
1801 FTQEDEQLVQEKMEALKATLPCSGLGISEEERVQIVSAIGYPRGHWFKCR 1850
1851 NGHIYVIGDCGGAMERGTCPDCKEVIGGTNHTLERSNQLASEMDGAQHAA 1900
1901 WSDTANNLMNFEEIQGMM 1918
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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