 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55162 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MARPIFPNQQKIAEKLIILNDRGLGILTRIYNIKKACGDTKSKPGFLSEK 50
51 SLESSIKFIVKRFPNIDVKGLNAIVNIKAEIIKSLSLYYHTFVDLLDFKD 100
101 NVCELLTTMDACQIHLDITLNFELTKYYLDLVVTYVSLMIVLSRVEDRKA 150
151 VLGLYNAAYELQNNQADTGFPRLGQMILDYEVPLKKLAEEFIPHQRLLTS 200
201 ALRSLTSIYALRNLPADKWREMQKLSLVGNPAILLKAVRTDTMSCEYISL 250
251 EAMDRWIIFGLLLNHQMLGQYPEVNKIWLSALESSWVVALFRDEVLQIHQ 300
301 YIQATFDGIKGYSKRIGEVKEAYNTAVQKAALMHRERRKFLRTALKELAL 350
351 IMTDQPGLLGPKAIFIFIGLCLARDEILWLLRHNDNPPLLKNKGKSNEDL 400
401 VDRQLPELLFHMEELRALVRKYSQVMQRYYVQYLSGFDATDLNIRMQSLQ 450
451 MCPEDESIIFSSLYNTAAALTVKQVEDNELFYFRPFRLDWFRLQTYMSVG 500
501 KAALRIAEHAELARLLDSMVFHTRVVDNLDEILVETSDLSIFCFYNKMFD 550
551 DQFHMCLEFPAQNRYIIAFPLICSHFQNCTHEMCPEERHHIRERSLSVVN 600
601 IFLEEMAKEAKNIITTICDEQCTMADALLPKHCAKILSVQSARKKKDKSK 650
651 SKHFDDIRKPGDESYRKTREDLTTMDKLHMALTELCFAINYCPTVNVWEF 700
701 AFAPREYLCQNLEHRFSRDLVGMVMFNQETMEIAKPSELLASVRAYMNVL 750
751 QTVENYVHIDITRVFNNCLLQQTQALDSHGEKTIAALYNTWYSEVLLRRV 800
801 SAGNIVFSINQKAFVPISPEGWVPFNPQEFSDLNELRALAELVGPYGIKT 850
851 LNETLMWHIANQVQELKSLVSTNKEVLITLRTSFDKPEVMKEQFKRLQDV 900
901 DRVLQRMTIIGVIICFRNLVHEALVDVLDKRIPFLLSSVKDFQEHLPGGD 950
951 QIRVASEMASAAGLLCKVDPTLATTLKSKKPEFDEGEHLTACLLMVFVAV 1000
1001 SIPKLARNENSFYRATIDGHSNNTHCMAAAINNIFGALFTICGQSDMEDR 1050
1051 MKEFLALASSSLLRLGQESDKEATRNRESIYLLLDEIVKQSPFLTMDLLE 1100
1101 SCFPYVLIRNAYHGVYKQEQILGLAL 1126
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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