 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P42945 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MSSLSDQLAQVASNNATVALDRKRRQKLHSASLIYNSKTAATQDYDFIFE 50
51 NASKALEELSQIEPKFAIFSRTLFSESSISLDRNVQTKEEIKDLDNAINA 100
101 YLLLASSKWYLAPTLHATEWLVRRFQIHVKNTEMLLLSTLNYYQTPVFKR 150
151 ILSIIKLPPLFNCLSNFVRSEKPPTALTMIKLFNDMDFLKLYTSYLDQCI 200
201 KHNATYTNQLLFTTCCFINVVAFNSNNDEKLNQLVPILLEISAKLLASKS 250
251 KDCQIAAHTILVVFATALPLKKTIILAAMETILSNLDAKEAKHSALLTIC 300
301 KLFQTLKGQGNVDQLPSKIFKLFDSKFDTVSILTFLDKEDKPVCDKFITS 350
351 YTRSIARYDRSKLNIILSLLKKIRLERYEVRLIITDLIYLSEILEDKSQL 400
401 VELFEYFISINEDLVLKCLKSLGLTGELFEIRLTTSLFTNADVNTDIVKQ 450
451 LSDPVETTKKDTASFQTFLDKHSELINTTNVSMLTETGERYKKVLSLFTE 500
501 AIGKGYKASSFLTSFFTTLESRITFLLRVTISPAAPTALKLISLNNIAKY 550
551 INSIEKEVNIFTLVPCLICALRDASIKVRTGVKKILSLIAKRPSTKHYFL 600
601 SDKLYGENVTIPMLNPKDSEAWLSGFLNEYVTENYDISRILTPKRNEKVF 650
651 LMFWANQALLIPSPYAKTVLLDNLNKSPTYASSYSSLFEEFISHYLENRS 700
701 SWEKSCIANKTNFEHFERSLVNLVSPKEKQSFMIDFVLSALNSDYEQLAN 750
751 IAAERLISIFASLNNAQKLKIVQNIVDSSSNVESSYDTVGVLQSLPLDSD 800
801 IFVSILNQNSISNEMDQTDFSKRRRRRSSTSKNAFLKEEVSQLAELHLRK 850
851 LTIILEALDKVRNVGSEKLLFTLLSLLSDLETLDQDGGLPVLYAQETLIS 900
901 CTLNTITYLKEHGCTELTNVRADILVSAIRNSASPQVQNKLLLVIGSLAT 950
951 LSSEVILHSVMPIFTFMGAHSIRQDDEFTTKVVERTILTVVPALIKNSKG 1000
1001 NEKEEMEFLLLSFTTALQHVPRHRRVKLFSTLIKTLDPVKALGSFLFLIA 1050
1051 QQYSSALVNFKIGEARILIEFIKALLVDLHVNEELSGLNDLLDIIKLLTS 1100
1101 SKSSSEKKKSLESRVLFSNGVLNFSESEFLTFMNNTFEFINKITEETDQD 1150
1151 YYDVRRNLRLKVYSVLLDETSDKKLIRNIREEFGTLLEGVLFFINSVELT 1200
1201 FSCITSQENEEASDSETSLSDHTTEIKEILFKVLGNVLQILPVDEFVNAV 1250
1251 LPLLSTSTNEDIRYHLTLVIGSKFELEGSEAIPIVNNVMKVLLDRMPLES 1300
1301 KSVVISQVILNTMTALVSKYGKKLEGSILTQALTLATEKVSSDMTEVKIS 1350
1351 SLALITNCVQVLGVKSIAFYPKIVPPSIKLFDASLADSSNPLKEQLQVAI 1400
1401 LLLFAGLIKRIPSFLMSNILDVLHVIYFSREVDSSIRLSVISLIIENIDL 1450
1451 KEVLKVLFRIWSTEIATSNDTVAVSLFLSTLESTVENIDKKSATSQSPIF 1500
1501 FKLLLSLFEFRSISSFDNNTISRIEASVHEISNSYVLKMNDKVFRPLFVI 1550
1551 LVRWAFDGEGVTNAGITETERLLAFFKFFNKLQENLRGIITSYFTYLLEP 1600
1601 VDMLLKRFISKDMENVNLRRLVINSLTSSLKFDRDEYWKSTSRFELISVS 1650
1651 LVNQLSNIENSIGKYLVKAIGALASNNSGVDEHNQILNKLIVEHMKASCS 1700
1701 SNEKLWAIRAMKLIYSKIGESWLVLLPQLVPVIAELLEDDDEEIEREVRT 1750
1751 GLVKVVENVLGEPFDRYLD 1769
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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