 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P10039 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MGLPSQVLACAILGLLYQHASGGLIKRIIRQKRETGLNVTLPEDNQPVVF 50
51 NHVYNIKLPVGSLCSVDLDTASGDADLKAEIEPVKNYEEHTVNEGNQIVF 100
101 THRINIPRRACGCAAAPDIKDLLSRLEELEGLVSSLREQCASGAGCCPNS 150
151 QTAEGRLDTAPYCSGHGNYSTEICGCVCEPGWKGPNCSEPACPRNCLNRG 200
201 LCVRGKCICEEGFTGEDCSQAACPSDCNDQGKCVDGVCVCFEGYTGPDCG 250
251 EELCPHGCGIHGRCVGGRCVCHEGFTGEDCNEPLCPNNCHNRGRCVDNEC 300
301 VCDEGYTGEDCGELICPNDCFDRGRCINGTCFCEEGYTGEDCGELTCPNN 350
351 CNGNGRCENGLCVCHEGFVGDDCSQKRCPKDCNNRGHCVDGRCVCHEGYL 400
401 GEDCGELRCPNDCHNRGRCINGQCVCDEGFIGEDCGELRCPNDCHNRGRC 450
451 VNGQCECHEGFIGEDCGELRCPNDCNSHGRCVNGQCVCDEGYTGEDCGEL 500
501 RCPNDCHNRGRCVEGRCVCDNGFMGEDCGELSCPNDCHQHGRCVDGRCVC 550
551 HEGFTGEDCRERSCPNDCNNVGRCVEGRCVCEEGYMGIDCSDVSPPTELT 600
601 VTNVTDKTVNLEWKHENLVNEYLVTYVPTSSGGLDLQFTVPGNQTSATIH 650
651 ELEPGVEYFIRVFAILKNKKSIPVSARVATYLPAPEGLKFKSVRETSVQV 700
701 EWDPLSISFDGWELVFRNMQKKDDNGDITSSLKRPETSYMQPGLAPGQQY 750
751 NVSLHIVKNNTRGPGLSRVITTKLDAPSQIEAKDVTDTTALITWSKPLAE 800
801 IEGIELTYGPKDVPGDRTTIDLSEDENQYSIGNLRPHTEYEVTLISRRGD 850
851 MESDPAKEVFVTDLDAPRNLKRVSQTDNSITLEWKNSHANIDNYRIKFAP 900
901 ISGGDHTELTVPKGNQATTRATLTGLRPGTEYGIGVTAVRQDRESAPATI 950
951 NAGTDLDNPKDLEVSDPTETTLSLRWRRPVAKFDRYRLTYVSPSGKKNEM 1000
1001 EIPVDSTSFILRGLDAGTEYTISLVAEKGRHKSKPTTIKGSTEEEPELGN 1050
1051 LSVSETGWDGFQLTWTAADGAYENFVIQVQQSDNPEETWNITVPGGQHSV 1100
1101 NVTGLKANTPYNVTLYGVIRGYRTKPLYVETTTGAHPEVGELTVSDITPE 1150
1151 SFNLSWTTTNGDFDAFTIEIIDSNRLLEPMEFNISGNSRTAHISGLSPST 1200
1201 DFIVYLYGISHGFRTQAISAAATTEAEPEVDNLLVSDATPDGFRLSWTAD 1250
1251 DGVFDSFVLKIRDTKRKSDPLELIVPGHERTHDITGLKEGTEYEIELYGV 1300
1301 SSGRRSQPINSVATTVVGSPKGISFSDITENSATVSWTPPRSRVDSYRVS 1350
1351 YVPITGGTPNVVTVDGSKTRTKLVKLVPGVDYNVNIISVKGFEESEPISG 1400
1401 ILKTALDSPSGLVVMNITDSEALATWQPAIAAVDNYIVSYSSEDEPEVTQ 1450
1451 MVSGNTVEYDLNGLRPATEYTLRVHAVKDAQKSETLSTQFTTGLDAPKDL 1500
1501 SATEVQSETAVITWRPPRAPVTDYLLTYESIDGRVKEVILDPETTSYTLT 1550
1551 ELSPSTQYTVKLQALSRSMRSKMIQTVFTTTGLLYPYPKDCSQALLNGEV 1600
1601 TSGLYTIYLNGDRTQPLQVFCDMAEDGGGWIVFLRRQNGKEDFYRNWKNY 1650
1651 VAGFGDPKDEFWIGLENLHKISSQGQYELRVDLRDRGETAYAVYDKFSVG 1700
1701 DAKTRYRLRVDGYSGTAGDSMTYHNGRSFSTFDKDNDSAITNCALSYKGA 1750
1751 FWYKNCHRVNLMGRYGDNNHSQGVNWFHWKGHEYSIQFAEMKLRPSSFRN 1800
1801 LEGRRKRA 1808
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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