 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q91062 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MWKLLLVALAFALADAQFQPGKVYRYSYDAFSISGLPEPGVNRAGLSGEM 50
51 KIEIHGHTHNQATLKITQVNLKYFLGPWPSDSFYPLTAGYDHFIQQLEVP 100
101 VRFDYSAGRIGDIYAPPQVTDTAVNIVRGILNLFQLSLKKNQQTFELQET 150
151 GVEGICQTTYVVQEGYRTNEMAVVKTKDLNNCDHKVYKTMGTAYAERCPT 200
201 CQKMNKNLRSTAVYNYAIFDEPSGYIIKSAHSEEIQQLSVFDIKEGNVVI 250
251 ESRQKLILEGIQSAPAASQAASLQNRGGLMYKFPSSAITKMSSLFVTKGK 300
301 NLESEIHTVLKHLVENNQLSVHEDAPAKFLRLTAFLRNVDAGVLQSIWHK 350
351 LHQQKDYRRWILDAVPAMATSEALLFLKRTLASEQLTSAEATQIVYSTLS 400
401 NQQATRESLSYARELLHTSFIRNRPILRKTAVLGYGSLVFRYCANTVSCP 450
451 DELLQPLHDLLSQSSDRADEEEIVLALKALGNAGQPNSIKKIQRFLPGQG 500
501 KSLDEYSTRVQAEAIMALRNIAKRDPRKVQEIVLPIFLNVAIKSELRIRS 550
551 CIVFFESKPSVALVSMVAVRLRREPNLQVASFVYSQMRSLSRSSNPEFRD 600
601 VAAACSVAIKMLGSKLDRLGCRYSKAVHVDTFNARTMAGVSADYFRINSP 650
651 SGPLPRAVAAKIRGQGMGYASDIVEFGLRAEGLQELLYRGSQEQDAYGTA 700
701 LDRQTLLRSGQARSHVSSIHDTLRKLSDWKSVPEERPLASGYVKVHGQEV 750
751 VFAELDKKMMQRISQLWHSARSHHAAAQEQIRAVVSKLEQGMDVLLTKGY 800
801 VVSEVRYMQPVCIGIPMDLNLLVSGVTTNRANLHASFSQSLPADMKLADL 850
851 LATNIELRVAATTSMSQHAVAIMGLTTDLAKAGMQTHYKTSAGLGVNGKI 900
901 EMNARESNFKASLKPFQQKTVVVLSTMESIVFVRDPSGSRILPVLPPKMT 950
951 LDKGLISQQQQQPHHQQQPHQHGQDQARAAYQRPWASHEFSPAEQKQIHD 1000
1001 IMTARPVMRRKQHCSKSAALSSKVCFSARLRNAAFIRNALLYKITGDYVS 1050
1051 KVYVQPTSSKAQIQKVELELQAGPQAAEKVIRMVELVAKASKKSKKNSTI 1100
1101 TEEGVGETIISQLKKILSSDKDKDAKKPPGSSSSSSSSSSSSSSSSSSDK 1150
1151 SGKKTPRQGSTVNLAAKRASKKQRGKDSSSSSSSSSSSSDSSKSPHKHGG 1200
1201 AKRQHAGHGAPHLGPQSHSSSSSSSSSSSSSSASKSFSTVKPPMTRKPRP 1250
1251 ARSSSSSSSSDSSSSSSSSSSSSSSSSSSSSSSSESKSLEWLAVKDVNQS 1300
1301 AFYNFKYVPQRKPQTSRRHTPASSSSSSSSSSSSSSSSSSSDSDMTVSAE 1350
1351 SFEKHSKPKVVIVLRAVRADGKQQGLQTTLYYGLTSNGLPKAKIVAVELS 1400
1401 DLSVWKLCAKFRLSAHMKAKAAIGWGKNCQQYRAMLEASTGNLQSHPAAR 1450
1451 VDIKWGRLPSSLQRAKNALLENKAPVIASKLEMEIMPKKNQKHQVSVILA 1500
1501 AMTPRRMNIIVKLPKVTYFQQGILLPFTFPSPRFWDRPEGSQSDSLPAQI 1550
1551 ASAFSGIVQDPVASACELNEQSLTTFNGAFFNYDMPESCYHVLAQECSSR 1600
1601 PPFIVLIKLDSERRISLELQLDDKKVKIVSRNDIRVDGEKVPLRRLSQKN 1650
1651 QYGFLVLDAGVHLLLKYKDLRVSFNSSSVQVWVPSSLKGQTCGLCGRNDD 1700
1701 ELVTEMRMPNLEVAKDFTSFAHSWIAPDETCGGACALSRQTVHKESTSVI 1750
1751 SGSRENCYSTEPIMRCPATCSASRSVPVSVAMHCLPAESEAISLAMSEGR 1800
1801 PFSLSGKSEDLVTEMEAHVSCVA 1823
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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