 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55161 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MSRSVLQPSQQKLAEKLTILNDRGVGMLTRLYNIKKACGDPKAKPSYLID 50
51 KNLESAVKFIVRKFPAVETRNNNQQLAQLQKEKSEILKNLALYYFTFVDV 100
101 MEFKDHVCDLLNTIDVCQVFFDITVNFDLTKNYLDLTVTYTTLMILLSRI 150
151 EERKAIIGLYNYAHEMTHGASDREYPRLGQMIVDYEHPLKKMMEEFVPHS 200
201 KSLSDALISLQMVYPRRNLSADQWRNAQLLSLISAPSTMLNPAQSDTMPC 250
251 EYLSLDAMEKWIIFGFILCHGMLNTEATALNLWKLALQSSSCLSLFRDEV 300
301 FHIHKAAEDLFVNIRGYNKRINDIRECKEAAVSHAGSMHRERRKFLRSAL 350
351 KELATVLSDQPGLLGPKALFVFMALSFARDEIIWLLRHADNMPKKSADDF 400
401 IDKHIAELIFYMEELRAHVRKYGPVMQRYYVQYLSGFDAVVLNELVQNLS 450
451 VCPEDESIIMSSFVNTMTSLSVKQVEDGEVFDFRGMRLDWFRLQAYTSVS 500
501 KASLSLADHRELGKMMNTIIFHTKMVDSLVEMLVETSDLSIFCFYSRAFE 550
551 KMFQQCLELPSQSRYSIAFPLLCTHFMSCTHELCPEERHHIGDRSLSLCN 600
601 MFLDEMAKQARNLITDICTEQCTLSDQLLPKHCAKTISQAVNKKSKKQTG 650
651 KKGEPEREKPGVESMRKNRLVVTNLDKLHTALSELCFSINYVPNMAVWEH 700
701 TFTPREYLTSHLEIRFTKSIVGMTMYNQATQEIAKPSELLTSVREYMTVL 750
751 QSIENYVQIDITRVFNNVLLQQTQHLDSHGEPTITSLYTNWYLETLLRQV 800
801 SNGHIAYFPAMKAFVNLPTENELTFNAEEYSDISEMRSLSELLGPYGMKF 850
851 LSESLMWHISSQVAELKKLVVENVDVLTQMRTSFDKPDQMAALFKRLSSV 900
901 DSVLKRMTIIGVILSFRSLAQEALRDVLSYHIPFLVSSIEDFKDHIPRET 950
951 DMKVAMNVYELSSAAGLPCEIDPALVVALSSQKSENISPEEEYKIACLLM 1000
1001 VFVAVSLPTLASNVMSQYSPAIEGHCNNIHCLAKAINQIAAALFTIHKGS 1050
1051 IEDRLKEFLALASSSLLKIGQETDKTTTRNRESVYLLLDMIVQESPFLTM 1100
1101 DLLESCFPYVLLRNAYHAVYKQSVTSSA 1128
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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