 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q00402 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MSHNNRHKKNNDKDSSAGQYANSIDNSLSQESVSTNGVTRMANLKADECG 50
51 SGDEGDKTKRFSISSILSKRETKDVLPEFAGSSSHNGVLTANSSKDMNFT 100
101 LELSENLLVECRKLQSSNEAKNEQIKSLKQIKESLSDKIEELTNQKKSFM 150
151 KELDSTKDLNWDLESKLTNLSMECRQLKELKKKTEKSWNDEKESLKLLKT 200
201 DLEILTLTKNGMENDLSSQKLHYDKEISELKERILDLNNENDRLLISVSD 250
251 LTSEINSLQSNRTERIKIQKQLDDAKASISSLKRKVQKKYYQKQHTSDTT 300
301 VTSDPDSEGTTSEEDIFDIVIEIDHMIETGPSVEDISEDLVKKYSEKNNM 350
351 ILLSNDSYKNLLQKSESASKPKDDELMTKEVAENLNMIALPNDDNYSKKE 400
401 FSLESHIKYLEASGYKVLPLEEFENLNESLSNPSYNYLKEKLQALKKIPI 450
451 DQSTFNLLKEPTIDFLLPLTSKIDCLIIPTKDYNDLFESVKNPSIEQMKK 500
501 CLEAKNDLQSNICKWLEERNGCKWLSNDLYFSMVNKIETPSKQYLSDKAK 550
551 EYDQVLIDTKALEGLKNPTIDFLREKASASDYLLLKKEDYVSPSLEYLVE 600
601 HAKATNHHLLSDSAYEDLVKCKENPDMEFLKEKSAKLGHTVVSNEAYSEL 650
651 EKKLEQPSLEYLVEHAKATNHHLLSDSAYEDLVKCKENPDMEFLKEKSAK 700
701 LGHTVVSNEAYSELQRKYSELEKEVEQPSLAYLVEHAKATDHHLLSDSAY 750
751 EDLVKCKENPDVEFLKEKSAKLGHTVVSSEEYSELQRKYSELEKEVEQPS 800
801 LAYLVEHAKATDHHLLSDSAYEELVKCKENPDMEFLKEKSAKLGHTVVSN 850
851 EAYSELEKKLEQPSLAYLVEHAKATDHHLLSDSAYEDLVKCKENSDVEFL 900
901 KEKSAKLGHTVVSNEAYSELEKKLEQPSLAYLVEHAKATDHHLLSDSAYE 950
951 DLVKCKENPDMEFLKEKSAKLGHTVVSNEAYSELEKKLEQPSLEYLVEHA 1000
1001 KATNHHLLSDSAYEDLVKCKENPDMEFLKEKSAKLGHTVVSNEAYSELEK 1050
1051 KLEQPSLEYLVEHAKATNHHLLSDSAYEELVKCKENPDVEFLKEKSAKLG 1100
1101 HTVVSNEAYSELEKKLEQPSLEYLVEHAKATNHHLLSDSAYEELVKCKEN 1150
1151 PDVEFLKEKSAKLGHTVVSNEAYSELEKKLEQPSLAYLVEHAKATDHHLL 1200
1201 SDSAYEDLVKCKENPDVEFLKEKSAKLGHTVVSNEAYSELEKKLEQPSLA 1250
1251 YLVEHAKATDHHLLSDSAYEDLVKCKENPDMEFLKEKSAKLGHTVVSNEA 1300
1301 YSELEKKLEQPSLEYLVEHAKATNHHLLSDSAYEDLVKCKENPDMEFLKE 1350
1351 KSAKLGHTVVSNKEYSELEKKLEQPSLEYLVKHAEQIQSKIISISDFNTL 1400
1401 ANPSMEDMASKLQKLEYQIVSNDEYIALKNTMEKPDVELLRSKLKGYHII 1450
1451 DTTTYNELVSNFNSPTLKFIEEKAKSKGYRLIEPNEYLDLNRIATTPSKE 1500
1501 EIDNFCKQIGCYALDSKEYERLKNSLENPSKKFIEENAALLDLVLVDKTE 1550
1551 YQAMKDNASNKKSLIPSTKALDFVTMPAPQLASAEKSSLQKRTLSDIENE 1600
1601 LKALGYVAIRKENLPNLEKPIVDNASKNDVLNLCSKFSLVPLSTEEYDNM 1650
1651 RKEHTKILNILGDPSIDFLKEKCEKYQMLIISKHDYEEKQEAIENPGYEF 1700
1701 ILEKASALGYELVSEVELDRMKQMIDSPDIDYMQEKAARNEMVLLRNEEK 1750
1751 EALQKKIEYPSLTFLIEKAAGMNKILVDQIEYDETIRKCNHPTRMELEES 1800
1801 CHHLNLVLLDQNEYSTLREPLENRNVEDLINTLSKLNYIAIPNTIYQDLI 1850
1851 GKYENPNFDYLKDSLNKMDYVAISRQDYELMVAKYEKPQLDYLKISSEKI 1900
1901 DHIVVPLSEYNLMVTNYRNPSLSYLKEKAVLNNHILIKEDDYKNILAVSE 1950
1951 HPTVIHLSEKASLLNKVLVDKDDFATMSRSIEKPTIDFLSTKALSMGKIL 2000
2001 VNESTHKRNEKLLSEPDSEFLTMKAKEQGLIIISEKEYSELRDQIDRPSL 2050
2051 DVLKEKAAIFDSIIVENIEYQQLVNTTSPCPPITYEDLKVYAHQFGMELC 2100
2101 LQKPNKLSGAERAERIDEQSINTTSSNSTTTSSMFTDALDDNIEELNRVE 2150
2151 LQNNEDYTDIISKSSTVKDATIFIPAYENIKNSAEKLGYKLVPFEKSNIN 2200
2201 LKNIEAPLFSKDNDDTSVASSIDLDHLSRKAEKYGMTLISDQEFEEYHIL 2250
2251 KDNAVNLNGGMEEMNNPLSENQNLAAKTTNTAQEGAFQNTVPHNDMDNEE 2300
2301 VEYGPDDPTFTVRQLKKPAGDRNLILTSREKTLLSRDDNIMSQNEAVYGD 2350
2351 DISDSFVDESQEIKNDVDIIKTQAMKYGMLCIPESNFVGASYASAQDMSD 2400
2401 IVVLSASYYHNLMSPEDMKWNCVSNEELQAEVKKRGLQIALTTKEDKKGQ 2450
2451 ATASKHEYVSHKLNNKTSTVSTKSGAKKGLAEAAATTAYEDSESHPQIEE 2500
2501 QSHRTNHHKHHKRQQSLNSNSTSKTTHSSRNTPASRRDIVASFMSRAGSA 2550
2551 SRTASLQTLASLNEPSIIPALTQTVIGEYLFKYYPRLGPFGFESRHERFF 2600
2601 WVHPYTLTLYWSASNPILENPANTKTKGVAILGVESVTDPNPYPTGLYHK 2650
2651 SIVVTTETRTIKFTCPTRQRHNIWYNSLRYLLQRNMQGISLEDIADDPTD 2700
2701 NMYSGKIFPLPGENTKSSSKRLSASRRSVSTRSLRHRVPQSRSFGNLR 2748
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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