 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8K561 from www.uniprot.org...
The NucPred score for your sequence is 0.60 (see score help below)
1 MSQGPRTCSLLLVLLLSHGGAYQREPSPRQDLHPLLQKMAEEIIEGSYLN 50
51 ALLDLTLFERSHVWTADLSHRVLAYLNSKNVAFTIPSLQAVMEAHLEQYL 100
101 YQPQKLLEDLRATDNQQFHTAMKCLLEDKWGHLDLEDVVINLGDIRDEAL 150
151 QSPGVNRSLFLITLERCFQVLNALECVEVLGRVLRGSSGSFLQPDITERL 200
201 PQDLHEDAFKNLSAVFKDLYDQTSAHTQRALYSWMTGILRTPFNVTDGSV 250
251 SWVSAEKLWILGRYMVHLSFEEIMNISPIEIGLFISYDNATKQLDMVYDI 300
301 TPELAQAFLERIRCSSFDVRNISTIHRLGLLVCFYDGLELLDATLAQVLL 350
351 HQMLKCSRLRGFQAGVQKLKANLLDIATENQTLNETLGSLSDAVVGLTSS 400
401 QLESLSSDAVHSAISTLNQVTGWGRSQIVILSAKYLAQEKVLSFYNVCQM 450
451 GVLLAGVGTQAFYSMDHKDLWQVLRSPLSQDMSDLSPVQQQGVLGKLMEA 500
501 EDATSGIAEVPRALFKEVSLYDLWKESRFNATVLKAKELRRSQALFLYEF 550
551 LGKTTERPEELLSAGQLVKGVPCSHIDAMSDHLFLALFQYFDNNFSLLSP 600
601 DQVNCLAWKYWEVSRSSMPPFLLATLPSRFLSSIPPSRCVRFLISLGKRR 650
651 LETLVLDSDKRSVVVRKVQQCLDGVIADEYTVDIVGHLLCHLPASFIERG 700
701 ISPRAWAAALHGLRSCTALSSEQKAAVRVRLLEQWGPPENWTAETTKDLA 750
751 PFLAFFSGDELHTVATKFPEILQQTASKMVGVLLPKEFLWAVFESVQNSS 800
801 NESPSFDPTFGCHGVVTPSSDDIFKLAEANACWDPEVLLCMEEDTFIRNV 850
851 ELLGAVKGFSRAQLMALKEKAIQVWDLPSRWKEHHIVSLGRIALALSESE 900
901 LEQLDLSSIDTVASLGQQTEWTPGQAKSILQAFLEDSGYGIQDLKSFHLV 950
951 GFGPTLCAMDPTEIQLIKTSEFRAVVARIGTLFCSTPVLAGFKKKAEVVF 1000
1001 GRPTEWTSSILQELGTIAAGITKAELRMLNKELMTYFQPSAIRCLPGEVF 1050
1051 KELSTEQIASLGPQNAASVTHSQRLQLSSAQLQSLQRALDGAKTHSWQTD 1100
1101 PLSSSPTWPASTGSPTGEPASQALWLGCTLLLLTAKS 1137
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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