 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7TME2 from www.uniprot.org...
The NucPred score for your sequence is 0.59 (see score help below)
1 MWRVKTLNLGLSPSPQKGKPAMSTPLRELKLQPEALADSGKGPSMISALT 50
51 PYLCRLELKERCNNSSPVDFINTENNFLSEQFSHPSTHIEACQRESDPTP 100
101 ESNSLFHTLEEAIETVDDFVVDPRDDSIVESMVLLPFSLGQQQDLMLQAH 150
151 LDTTAERTKSSLNESLGLEDLVGKEVAPCVEDSLTEIVAIRPEQPTFQDP 200
201 PLGPSDTEDAPVDLVPSENVLNFSLARLSPSAVLAQDFSVDHVDPGEETV 250
251 ENRVLQEMETSFPTFPEEAELGDQAPAANAEAVSPLYLTSSLVEMGPREA 300
301 PGPTVEDASRIPGLESETWMSPLAWLEKGVNTSVMLQNLRQSLSFSSVLQ 350
351 DAAVGNTPLATCSVGTSFTPPAPLEVGTKDSTSETERLLLGCRPPDLATL 400
401 SRHDLEENLLNSLVLLEVLSHQLQAWKSQLTVPHREARDSSTQTDSSPCG 450
451 VTKTPKHLQDSKEIRQALLQARNVMQSWGLVSGDLLSLLHLSLTHVQEGR 500
501 VTVSQESQRSKTLVSSCSRVLKKLKAKLQSLKTECEEARHSKEMALKGKA 550
551 AAEAVLEAFRAHASQRISQLEQGLTSMQEFRGLLQEAQTQLIGLHTEQKE 600
601 LAQQTVSLSSALQQDWTSVQLNYGIWAALLSWSRELTKKLTAKSRQALQE 650
651 RDAAIEEKKQVVKEVEQVSAHLEDCKGQIEQLKLENSRLTADLSAQLQIL 700
701 TSTESQLKEVRSQHSRCVQDLAVKDELLCQLTQSNKEQATQWQKEEMELK 750
751 HIQAELLQQQAVLAKEVQDLRETVEFIDEESQVAHRELGQIESQLKVTLE 800
801 LLRERSLQCETLRDTVDSLRAELASTEAKHEKQALEKTHQHSQELRLLAE 850
851 QLQSLTLFLQAKLKENKAESEIILPSTGSAPAQEHPLSNDSSISEQTPTA 900
901 AVDEVPEPAPVPLLGSVKSAFTRVASMASFQPTETPDLEKSLAEMSTVLQ 950
951 ELKSLCSLLQESKEEATGVLQREICELHSRLQAQEEEHQEALKAKEADME 1000
1001 KLNQALCLLRKNEKELLEVIQKQNEKILGQIDKSGQLINLREEVTQLTQS 1050
1051 LRRAETETKVLQEALEGQLDPSCQLMATNWIQEKVFLSQEVSKLRVMFLE 1100
1101 MKTEKEQLMDKYLSHRHILEENLRRSDTELKKLDDTIQHVYETLLSIPET 1150
1151 MKSCKELQGLLEFLS 1165
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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