 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P08120 from www.uniprot.org...
The NucPred score for your sequence is 0.25 (see score help below)
1 MLPFWKRLLYAAVIAGALVGADAQFWKTAGTAGSIQDSVKHYNRNEPKFP 50
51 IDDSYDIVDSAGVARGDLPPKNCTAGYAGCVPKCIAEKGNRGLPGPLGPT 100
101 GLKGEMGFPGMEGPSGDKGQKGDPGPYGQRGDKGERGSPGLHGQAGVPGV 150
151 QGPAGNPGAPGINGKDGCDGQDGIPGLEGLSGMPGPRGYAGQLGSKGEKG 200
201 EPAKENGDYAKGEKGEPGWRGTAGLAGPQGFPGEKGERGDSGPYGAKGPR 250
251 GEHGLKGEKGASCYGPMKPGAPGIKGEKGEPASSFPVKPTHTVMGPRGDM 300
301 GQKGEPGLVGRKGEPGPEGDTGLDGQKGEKGLPGGPGDRGRQGNFGPPGS 350
351 TGQKGDRGEPGLNGLPGNPGQKGEPGRAGATGKPGLLGPPGPPGGGRGTP 400
401 GPPGPKGPRGYVGAPGPQGLNGVDGLPGPQGYNGQKGGAGLPGRPGNEGP 450
451 PGKKGEKGTAGLNGPKGSIGPIGHPGPPGPEGQKGDAGLPGYGIQGSKGD 500
501 AGIPGYPGLKGSKGERGFKGNAGAPGDSKLGRPGTPGAAGAPGQKGDAGR 550
551 PGTPGQKGDMGIKGDVGGKCSSCRAGPKGDKGTSGLPGIPGKDGARGPPG 600
601 ERGYPGERGHDGINGQTGPPGEKGEDGRTGLPGATGEPGKPALCDLSLIE 650
651 PLKGDKGYPGAPGAKGVQGFKGAEGLPGIPGPKGEFGFKGEKGLSGAPGN 700
701 DGTPGRAGRDGYPGIPGQSIKGEPGFHGRDGAKGDKGSFGRSGEKGEPGS 750
751 CALDEIKMPAKGNKGEPGQTGMPGPPGEDGSPGERGYTGLKGNTGPQGPP 800
801 GVEGPRGLNGPRGEKGNQGAVGVPGNPGKDGLRGIPGRNGQPGPRGEPGI 850
851 SRPGPMGPPGLNGLQGEKGDRGPTGPIGFPGADGSVGYPGDRGDAGLPGV 900
901 SGRPGIVGEKGDVGPIGPAGVAGPPGVPGIDGVRGRDGAKGEPGSPGLVG 950
951 MPGNKGDRGAPGNDGPKGFAGVTGAPGKRGPAGIPGVSGAKGDKGATGLT 1000
1001 GNDGPVGGRGPPGAPGLMGIKGDQGLAGAPGQQGLDGMPGEKGNQGFPGL 1050
1051 DGPPGLPGDASEKGQKGEPGPSGLRGDTGPAGTPGWPGEKGLPGLAVHGR 1100
1101 AGPPGEKGDQGRSGIDGRDGINGEKGEQGLQGVWGQPGEKGSVGAPGIPG 1150
1151 APGMDGLPGAAGAPGAVGYPGDRGDKGEPGLSGLPGLKGETGPVGLQGFT 1200
1201 GAPGPKGERGIRGQPGLPATVPDIRGDKGSQGERGYTGEKGEQGERGLTG 1250
1251 PAGVAGAKGDRGLQGPPGASGLNGIPGAKGDIGPRGEIGYPGVTIKGEKG 1300
1301 LPGRPGRNGRQGLIGAPGLIGERGLPGLAGEPGLVGLPGPIGPAGSKGER 1350
1351 GLAGSPGQPGQDGFPGAPGLKGDTGPQGFKGERGLNGFEGQKGDKGDRGL 1400
1401 QGPSGLPGLVGQKGDTGYPGLNGNDGPVGAPGERGFTGPKGRDGRDGTPG 1450
1451 LPGQKGEPGMLPPPGPKGEPGQPGRNGPKGEPGRPGERGLIGIQGERGEK 1500
1501 GERGLIGETGNVGRPGPKGDRGEPGERGYEGAIGLIGQKGEPGAPAPAAL 1550
1551 DYLTGILITRHSQSETVPACSAGHTELWTGYSLLYVDGNDYAHNQDLGSP 1600
1601 GSCVPRFSTLPVLSCGQNNVCNYASRNDKTFWLTTNAAIPMMPVENIEIR 1650
1651 QYISRCVVCEAPANVIAVHSQTIEVPDCPNGWEGLWIGYSFLMHTAVGNG 1700
1701 GGGQALQSPGSCLEDFRATPFIECNGAKGTCHFYETMTSFWMYNLESSQP 1750
1751 FERPQQQTIKAGERQSHVSRCQVCMKNSS 1779
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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