 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O94822 from www.uniprot.org...
The NucPred score for your sequence is 0.82 (see score help below)
1 MGGKNKQRTKGNLRPSNSGRAAELLAKEQGTVPGFIGFGTSQSDLGYVPA 50
51 IQGAEEIDSLVDSDFRMVLRKLSKKDVTTKLKAMQEFGTMCTERDTETVK 100
101 GVLPYWPRIFCKISLDHDRRVREATQQAFEKLILKVKKQLAPYLKSLMGY 150
151 WLMAQCDTYTPAAFAAKDAFEAAFPPSKQPEAIAFCKDEITSVLQDHLIK 200
201 ETPDTLSDPQTVPEEEREAKFYRVVTCSLLALKRLLCLLPDNELDSLEEK 250
251 FKSLLSQNKFWKYGKHSVPQIRSAYFELVSALCQRIPQLMKEEASKVSPS 300
301 VLLSIDDSDPIVCPALWEAVLYTLTTIEDCWLHVNAKKSVFPKLSTVIRE 350
351 GGRGLATVIYPYLLPFISKLPQSITNPKLDFFKNFLTSLVAGLSTERTKT 400
401 SSLESSAVISAFFECLRFIMQQNLGEEEIEQMLVNDQLIPFIDAVLKDPG 450
451 LQHGQLFNHLAETLSSWEAKADTEKDEKTAHNLENVLIHFWERLSEICVA 500
501 KISEPEADVESVLGVSNLLQVLQKPKSSLKSSKKKNGKVRFADEILESNK 550
551 ENEKCVSSEGEKIEGWELTTEPSLTHNSSGLLSPLRKKPLEDLVCKLADI 600
601 SINYVNERKSEQHLRFLSTLLDSFSSSRVFKMLLGDEKQSIVQAKPLEIA 650
651 KLVQKNPAVQFLYQKLIGWLNEDQRKDFGFLVDILYSALRCCDNDMERKK 700
701 VLDDLTKVDLKWNSLLKIIEKACPSSDKHALVTPWLKGDILGEKLVNLAD 750
751 CLCNEDLESRVSSESHFSERWTLLSLVLSQHVKNDYLIGDVYVERIIVRL 800
801 HETLFKTKKLSEAESSDSSVSFICDVAYNYFSSAKGCLLMPSSEDLLLTL 850
851 FQLCAQSKEKTHLPDFLICKLKNTWLSGVNLLVHQTDSSYKESTFLHLSA 900
901 LWLKNQVQASSLDINSLQVLLSAVDDLLNTLLESEDSYLMGVYIGSVMPN 950
951 DSEWEKMRQSLPMQWLHRPLLEGRLSLNYECFKTDFKEQDIKTLPSHLCT 1000
1001 SALLSKMVLIALRKETVLENNELEKIIAELLYSLQWCEELDNPPIFLIGF 1050
1051 CEILQKMNITYDNLRVLGNTSGLLQLLFNRSREHGTLWSLIIAKLILSRS 1100
1101 ISSDEVKPHYKRKESFFPLTEGNLHTIQSLCPFLSKEEKKEFSAQCIPAL 1150
1151 LGWTKKDLCSTNGGFGHLAIFNSCLQTKSIDDGELLHGILKIIISWKKEH 1200
1201 EDIFLFSCNLSEASPEVLGVNIEIIRFLSLFLKYCSSPLAESEWDFIMCS 1250
1251 MLAWLETTSENQALYSIPLVQLFACVSCDLACDLSAFFDSTTLDTIGNLP 1300
1301 VNLISEWKEFFSQGIHSLLLPILVTVTGENKDVSETSFQNAMLKPMCETL 1350
1351 TYISKEQLLSHKLPARLVADQKTNLPEYLQTLLNTLAPLLLFRARPVQIA 1400
1401 VYHMLYKLMPELPQYDQDNLKSYGDEEEEPALSPPAALMSLLSIQEDLLE 1450
1451 NVLGCIPVGQIVTIKPLSEDFCYVLGYLLTWKLILTFFKAASSQLRALYS 1500
1501 MYLRKTKSLNKLLYHLFRLMPENPTYAETAVEVPNKDPKTFFTEELQLSI 1550
1551 RETTMLPYHIPHLACSVYHMTLKDLPAMVRLWWNSSEKRVFNIVDRFTSK 1600
1601 YVSSVLSFQEISSVQTSTQLFNGMTVKARATTREVMATYTIEDIVIELII 1650
1651 QLPSNYPLGSIIVESGKRVGVAVQQWRNWMLQLSTYLTHQNGSIMEGLAL 1700
1701 WKNNVDKRFEGVEDCMICFSVIHGFNYSLPKKACRTCKKKFHSACLYKWF 1750
1751 TSSNKSTCPLCRETFF 1766
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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