 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O19179 from www.uniprot.org...
The NucPred score for your sequence is 0.14 (see score help below)
1 MSACALLAGGLPDPRLCAPARWARSPPGVPGAPPWPQPRLRLLLLLLLLP 50
51 PSALSAVFTVGVLGPWACDPIFARARPDLAARLAAARLNRDAALEDGPRF 100
101 EVTLLPEPCRTPGSLGAVSSALGRVSGLVGPVNPAACRPAELLAQEAGVA 150
151 LVPWSCPGTRAGGTTAPAGTPAADALYALLRAFRWARVALITAPQDLWVE 200
201 AGRALSAALRARGLPVALVTTMEPSDLSGAREALRRVQDGPRVRAVIMVM 250
251 HSVLLGGEEQRCLLQAAEELGLADGSLVFLPFDTLHYALSPGPEALAVLA 300
301 NSSQLRRAHDAVLILTRHCPPGGSVMDNLRRAQEHQELPSDLDLQQVSPF 350
351 FGTIYDAVLLLAGGVARARAAAGGGWVSGATVAHHIPDAQVPGFCGTLGG 400
401 AQEPPFVLLDTDAAGDRLFATYMLDPTRGSLLSAGTPVHFPRGGGTPGSD 450
451 PSCWFEPGVICNGGVEPGLVFLGFLLVVGMGLTGAFLAHYLRHRLLHIQM 500
501 VSGPNKIILTLDDVTFLHPHGGSTRKVVQGSRSSLAARSTSDIRSVPSQP 550
551 LDNSNIGLFEGDWVWLKKFPGDQHIAIRPATKTAFSKLRELRHENVVLYL 600
601 GLFLGSGGAGGSAAGEGVLAVVSEHCARGSLHDLLAQRDIKLDWMFKSSL 650
651 LLDLIKGMRYLHHRGVAHGRLKSRNCVVDGRFVLKVTDHGHARLMEAQRV 700
701 LLEPPSAEDQLWTAPELLRDPALERRGTLPGDVFSLGIIMQEVVCRSAPY 750
751 AMLELTPEEVVERVRSPPPLCRPSVSMDQAPVECIQLMKQCWAEHPDLRP 800
801 SLGHIFDQFKSINKGRKTNIIDSMLRMLEQYSSNLEDLIRERTEELELEK 850
851 QKTDRLLTQMLPPSVAEALKMGTPVEPEYFEEVTLYFSDIVGFTTISAMS 900
901 EPIEVVDLLNDLYTLFDAIIGSHDVYKVETIGDAYMVASGLPQRNGQRHA 950
951 AEIANMALDILSAVGSFRMRHMPEVPVRIRIGLHSGPCVAGVVGLTMPRY 1000
1001 CLFGDTVNTASRMESTGLPYRIHVNMSTVRILHALDEGFQTEVRGRTELK 1050
1051 GKGAEDTYWLVGRRGFNKPIPKPPDLQPGASNHGISLQEIPLDRRWKLEK 1100
1101 ARPGQFSGK 1109
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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