 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q60467 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MDVHTRWKDRLPVGPAAVPPLLLLLLLLWAPPQSRAAQPTDLLEMLDFHN 50
51 LPSGVTKTTGFCATRRSSRGPDVAYRVSKDAQLSMPRKQLYPDSDFAEDF 100
101 SILTTVKAKKGSQAFLVSVYNEQGIQQVGMELGRSPVFLYEDHTGKPGPE 150
151 EYPLFPGINLSDGKWHRIAISVYRKNVTLILDCKKKVVKFLNRSDHPIID 200
201 VNGIIMFGSRILDDEIFEGDIQQLLFVSDHRAAYDYCEHYSPDCDTAVPD 250
251 TPQSQDPNPDEYYPEGDGETYYYEYPYYEDPEDLGKEPAPTQKPVEAARE 300
301 TTEVPEEQTQPPPEAPTVPETSDPAGKEDDPGFGDYDYVPTDDYYTPAPY 350
351 EDLGYGEGVENPDQPTNPDSGAEVPTSTIITSNTSNPAPSPGEDKDDLGG 400
401 EFTEETIKNLEENYYDPYFDPDSDSNVSPSEIGPGMPANQDTIYEGIGGP 450
451 RGEKGQKGEPAIIEPGMLIEGPPGPEGPAGLPGPPGTTGPTGQVGDPGER 500
501 GPPGRPGLPGADGLPGPPGTMLMLPFRFGGGGDAGSKGPMVSAQESQAQA 550
551 ILQQARLALRGPAGPMGLTGRPGPMGPPGSGGLKGEPGDMGPQGPRGVQG 600
601 PPGPTGKPGRRGRAGSDGARGMPGQTGPKGDRGFDGLAGLPGEKGHRGDP 650
651 GPSGPPGLPGDDGERGDDGEVGPRGLPGEPGPRGLLGPKGPPGPPGPPGV 700
701 TGMDGQPGPKGNVGPQGEPGPPGQQGNPGAQGLPGPQGAIGPPGEKGPLG 750
751 KPGLPGMPGADGPPGHPGKEGPPGEKGGQGPPGPQGPIGYPGPRGVKGAD 800
801 GIRGLKGTKGEKGEDGFPGFKGDMGIKGDRGEIGPPGPRGEDGPEGPKGR 850
851 GGPNGDPGPLGPTGEKGKLGVPGLPGYPGRQGPKGSIGFPGFPGANGEKG 900
901 GRGTPGKPGPRGQRGPTGPRGERGPRGITGKPGPKGNSGGDGPAGPPGER 950
951 GPNGPQGPTGFPGPKGPPGPPGKDGLPGHPGQRGETGFQGKTGPPGPPGV 1000
1001 VGPQGPTGETGPMGERGHPGPPGPPGEQGLPGVAGKEGTKGDPGPAGLPG 1050
1051 KDGPPGLRGFPGDRGLPGPVGALGLKGSEGPPGPPGPAGSPGERGPAGAA 1100
1101 GPIGIPGRPGPQGPPGPAGEKGVPGEKGPQGPAGRDGLQGPVGLPGPAGP 1150
1151 VGPPGEDGDKGEIGEPGQKGSKGDKGEQGPPGPTGPQGPIGQPGPSGADG 1200
1201 EPGPRGQQGLFGQKGDEGSRGFPGPPGPVGLQGLPGPPGEKGETGDVGQM 1250
1251 GPPGPPGPRGPSGAPGADGPQGPPGGIGNPGAVGEKGEPGEAGEPGLPGE 1300
1301 GGPLGPKGERGEKGEVGPSGAAGPPGPKGPPGDDGPKGSPGPVGFPGDPG 1350
1351 PPGEPGPAGQDGPPGDKGDDGEPGQTGSPGPTGEPGPSGPPGKRGPPGPA 1400
1401 GPEGRQGEKGAKGEAGLEGPPGKTGPIGPQGAPGKPGPDGLRGIPGPVGE 1450
1451 QGLPGSPGPDGPPGPMGPPGLPGLKGDSGPKGEKGHPGLIGLIGPPGEQG 1500
1501 EKGDRGLPGPQGSSGPKGEQGITGPSGPLGPPGPPGLPGPPGPKGAKGSS 1550
1551 GPTGPKGEAGHPGLPGPPGPPGEVIQPLPIQASRTRRNIDASQLLDDGAG 1600
1601 ESYLDYADGMEEIFGSLNSLKLEIEQMKRPLGTQQNPARTCKDLQLCHPD 1650
1651 FPDGEYWVDPNQGCSRDSFKVYCNFTAGGSTCVFPDKKSEGARITSWPKE 1700
1701 NPGSWFSEFKRGKLLSYVDAEGNPVGVVQMTFLRLLSASAHQNITYNCYQ 1750
1751 SVAWQDAATGSYDKAIRFLGSNDEEMSYDNNPYIRALVDGCATKKGYQKT 1800
1801 VLEIDTPKVEQVPIVDIMFNDFGEASQKFGFEVGPACFLG 1840
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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