 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P39061 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MAPDPSRRLCLLLLLLLSCRLVPASADGNSLSPLNPLVWLWPPKTSDSLE 50
51 GPVSKPQNSSPVQSTENPTTHVVPQDGLTEQQTTPASSELPPEEEEEEDQ 100
101 KAGQGGSPATPAVPIPLVAPAASPDMKEENVAGVGAKILNVAQGIRSFVQ 150
151 LWDEDSTIGHSAGTEVPDSSIPTVLPSPAELSSAPQGSKTTLWLSSAIPS 200
201 SPDAQTTEAGTLAVPTQLPPFQSNLQAPLGRPSAPPDFPGRAFLSSSTDQ 250
251 GSSWGNQEPPRQPQHLEGKGFLPMTARSSQQHRHSDVHSDIHGHVPLLPL 300
301 VTGPLVTASLSVHGLLSVPSSDPSGQLSQVAALPGFPGTWVSHVAPSSGT 350
351 GLSNDSALAGNGSLTSTSRCLPLPPTLTLCSRLGIGHFWLPNHLHHTDSV 400
401 EVEATVQAWGRFLHTNCHPFLAWFFCLLLAPSCGPGPPPPLPPCRQFCEA 450
451 LEDECWNYLAGDRLPVVCASLPSQEDGYCVFIGPAAENVAEEVGLLQLLG 500
501 DPLPEKISQIDDPHVGPAYIFGPDSNSGQVAQYHFPKLFFRDFSLLFHVR 550
551 PATEAAGVLFAITDAAQVVVSLGVKLSEVRDGQQNISLLYTEPGASQTQT 600
601 GASFRLPAFVGQWTHFALSVDGGSVALYVDCEEFQRVPFARASQGLELER 650
651 GAGLFVGQAGTADPDKFQGMISELKVRKTPRVSPVHCLDEEDDDEDRASG 700
701 DFGSGFEESSKSHKEDTSLLPGLPQPPPVTSPPLAGGSTTEDPRTEETEE 750
751 DAAVDSIGAETLPGTGSSGAWDEAIQNPGRGLIKGGMKGQKGEPGAQGPP 800
801 GPAGPQGPAGPVVQSPNSQPVPGAQGPPGPQGPPGKDGTPGRDGEPGDPG 850
851 EDGRPGDTGPQGFPGTPGDVGPKGEKGDPGIGPRGPPGPPGPPGPSFRQD 900
901 KLTFIDMEGSGFSGDIESLRGPRGFPGPPGPPGVPGLPGEPGRFGINGSY 950
951 APGPAGLPGVPGKEGPPGFPGPPGPPGPPGKEGPPGVAGQKGSVGDVGIP 1000
1001 GPKGSKGDLGPIGMPGKSGLAGSPGPVGPPGPPGPPGPPGPGFAAGFDDM 1050
1051 EGSGIPLWTTARSSDGLQGPPGSPGLKGDPGVAGLPGAKGEVGADGAQGI 1100
1101 PGPPGREGAAGSPGPKGEKGMPGEKGNPGKDGVGRPGLPGPPGPPGPVIY 1150
1151 VSSEDKAIVSTPGPEGKPGYAGFPGPAGPKGDLGSKGEQGLPGPKGEKGE 1200
1201 PGTIFSPDGRALGHPQKGAKGEPGFRGPPGPYGRPGHKGEIGFPGRPGRP 1250
1251 GTNGLKGEKGEPGDASLGFSMRGLPGPPGPPGPPGPPGMPIYDSNAFVES 1300
1301 GRPGLPGQQGVQGPSGPKGDKGEVGPPGPPGQFPIDLFHLEAEMKGDKGD 1350
1351 RGDAGQKGERGEPGAPGGGFFSSSVPGPPGPPGYPGIPGPKGESIRGPPG 1400
1401 PPGPQGPPGIGYEGRQGPPGPPGPPGPPSFPGPHRQTVSVPGPPGPPGPP 1450
1451 GPPGAMGASAGQVRIWATYQTMLDKIREVPEGWLIFVAEREELYVRVRNG 1500
1501 FRKVLLEARTALPRGTGNEVAALQPPLVQLHEGSPYTRREYSYSTARPWR 1550
1551 ADDILANPPRLPDRQPYPGVPHHHSSYVHLPPARPTLSLAHTHQDFQPVL 1600
1601 HLVALNTPLSGGMRGIRGADFQCFQQARAVGLSGTFRAFLSSRLQDLYSI 1650
1651 VRRADRGSVPIVNLKDEVLSPSWDSLFSGSQGQLQPGARIFSFDGRDVLR 1700
1701 HPAWPQKSVWHGSDPSGRRLMESYCETWRTETTGATGQASSLLSGRLLEQ 1750
1751 KAASCHNSYIVLCIENSFMTSFSK 1774
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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