 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P08122 from www.uniprot.org...
The NucPred score for your sequence is 0.22 (see score help below)
1 MDRVRFKASGPPLRGWLLLATVTVGLLAQSVLGGVKKLDVPCGGRDCSGG 50
51 CQCYPEKGARGQPGAVGPQGYNGPPGLQGFPGLQGRKGDKGERGVPGPTG 100
101 PKGDVGARGVSGFPGADGIPGHPGQGGPRGRPGYDGCNGTRGDAGPQGPS 150
151 GSGGFPGLPGPQGPKGQKGEPYALSKEDRDKYRGEPGEPGLVGYQGPPGR 200
201 PGPIGQMGPMGAPGRPGPPGPPGPKGQPGNRGLGFYGQKGEKGDIGQPGP 250
251 NGIPSDITLVGPTTSTIHPDLYKGEKGDEGEQGIPGVISKGEEGIMGFPG 300
301 IRGFPGLDGEKGVVGQKGSRGLDGFQGPSGPRGPKGERGEQGPPGPSVYS 350
351 PHPSLAKGARGDPGFQGAHGEPGSRGEPGEPGTAGPPGPSVGDEDSMRGL 400
401 PGEMGPKGFSGEPGSPARYLGPPGADGRPGPQGVPGPAGPPGPDGFLFGL 450
451 KGSEGRVGYPGPSGFPGTRGQKGWKGEAGDCQCGQVIGGLPGLPGPKGFP 500
501 GVNGELGKKGDQGDPGLHGIPGFPGFKGAPGVAGAPGPKGIKGDSRTITT 550
551 KGERGQPGIPGVHGMKGDDGVPGRDGLDGFPGLPGPPGDGIKGPPGDAGL 600
601 PGVPGTKGFPGDIGPPGQGLPGPKGERGFPGDAGLPGPPGFPGPPGPPGT 650
651 PGQRDCDTGVKRPIGGGQQVVVQPGCIEGPTGSPGQPGPPGPTGAKGVRG 700
701 MPGFPGASGEQGLKGFPGDPGREGFPGPPGFMGPRGSKGTTGLPGPDGPP 750
751 GPIGLPGPAGPPGDRGIPGEVLGAQPGTRGDAGLPGQPGLKGLPGETGAP 800
801 GFRGSQGMPGMPGLKGQPGFPGPSGQPGQSGPPGQHGFPGTPGREGPLGQ 850
851 PGSPGLGGLPGDRGEPGDPGVPGPVGMKGLSGDRGDAGMSGERGHPGSPG 900
901 FKGMAGMPGIPGQKGDRGSPGMDGFQGMLGLKGRQGFPGTKGEAGFFGVP 950
951 GLKGLPGEPGVKGNRGDRGPPGPPPLILPGMKDIKGEKGDEGPMGLKGYL 1000
1001 GLKGIQGMPGVPGVSGFPGLPGRPGFIKGVKGDIGVPGTPGLPGFPGVSG 1050
1051 PPGITGFPGFTGSRGEKGTPGVAGVFGETGPTGDFGDIGDTVDLPGSPGL 1100
1101 KGERGITGIPGLKGFFGEKGAAGDIGFPGITGMAGAQGSPGLKGQTGFPG 1150
1151 LTGLQGPQGEPGRIGIPGDKGDFGWPGVPGLPGFPGIRGISGLHGLPGTK 1200
1201 GFPGSPGVDAHGDPGFPGPTGDRGDRGEANTLPGPVGVPGQKGERGTPGE 1250
1251 RGPAGSPGLQGFPGISPPSNISGSPGDVGAPGIFGLQGYQGPPGPPGPNA 1300
1301 LPGIKGDEGSSGAAGFPGQKGWVGDPGPQGQPGVLGLPGEKGPKGEQGFM 1350
1351 GNTGPSGAVGDRGPKGPKGDQGFPGAPGSMGSPGIPGIPQKIAVQPGTLG 1400
1401 PQGRRGLPGALGEIGPQGPPGDPGFRGAPGKAGPQGRGGVSAVPGFRGDQ 1450
1451 GPMGHQGPVGQEGEPGRPGSPGLPGMPGRSVSIGYLLVKHSQTDQEPMCP 1500
1501 VGMNKLWSGYSLLYFEGQEKAHNQDLGLAGSCLARFSTMPFLYCNPGDVC 1550
1551 YYASRNDKSYWLSTTAPLPMMPVAEEEIKPYISRCSVCEAPAVAIAVHSQ 1600
1601 DTSIPHCPAGWRSLWIGYSFLMHTAAGDEGGGQSLVSPGSCLEDFRATPF 1650
1651 IECNGGRGTCHYFANKYSFWLTTIPEQNFQSTPSADTLKAGLIRTHISRC 1700
1701 QVCMKNL 1707
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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