 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55203 from www.uniprot.org...
The NucPred score for your sequence is 0.18 (see score help below)
1 MTACTFLAGGLRDPGLCGPTRWAPSPPGLPPIPPRPRLRLRPPLLLLLLL 50
51 PRSVLSAVFTVGVLGPWACDPIFARARPDLAARLAASRLNHAAALEGGPR 100
101 FEVALLPEPCRTPGSLGAVSSALTRVSGLVGPVNPAACRPAELLAQEAGV 150
151 ALVPWGCPGTRAAGTTAPVVTPAADALYALLRAFRWAHVALVTAPQDLWV 200
201 EAGHALSTALRARGLPVALVTSMEPSDLSGAREALRRVQDGPRVRAVIMV 250
251 MHSVLLGGEEQRCLLEAAEELGLADGSLVFLPFDTLHYALSPGPDALAVL 300
301 ANSSQLRKAHDAVLTLTRHCPLGGSVRDSLRRAQEHRELPLDLNLQQVSP 350
351 LFGTIYDSVFLLAGGVARARVAAGGGWVSGAAVARHIRDARVPGFCGALG 400
401 GAEEPSFVLLDTDATGDQLFATYVLDPTQGFFHSAGTPVHFPKGGRGPGP 450
451 DPSCWFDPDTICNGGVEPSVVFIGFLLVVGMGLAGAFLAHYCRHRLLHIQ 500
501 MVSGPNKIILTLDDITFLHPHGGNSRKVAQGSRTSLAARSISDVRSIHSQ 550
551 LPDYTNIGLYEGDWVWLKKFPGDRHIAIRPATKMAFSKIRELRHENVALY 600
601 LGLFLAGGAGGPAAPGEGVLAVVSEHCARGSLQDLLAQRDIKLDWMFKSS 650
651 LLLDLIKGIRYLHHRGVAHGRLKSRNCVVDGRFVLKVTDHGHGRLLEAQR 700
701 VLPEPPSAEDQLWTAPELLRDPVLERRGTLAGDVFSLGIIMQEVVCRSAP 750
751 YAMLELTPEEVVKRVQSPPPLCRPSVSIDQAPMECIQLMKQCWAEQPELR 800
801 PSMDRTFELFKSINKGRKMNIIDSMLRMLEQYSSNLEDLIRERTEELELE 850
851 KQKTDRLLTQMLPPSVAEALKMGTPVEPEYFEEVTLYFSDIVGFTTISAM 900
901 SEPIEVVDLLNDLYTLFDAIIGSHDVYKVETIGDAYMVASGLPQRNGHRH 950
951 AAEIANMALDILSAVGTFRMRHMPEVPVRIRIGLHSGPCVAGVVGLTMPR 1000
1001 YCLFGDTVNTASAMESTGLPYRIHVNRSTVQILSALNEGFLTEVRGRTEL 1050
1051 KGKGAEETYWLVGRRGFNKPIPKPPDLQPGASNHGISLHEIPPDRRQKLE 1100
1101 KARPGQFSGK 1110
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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