 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O43451 from www.uniprot.org...
The NucPred score for your sequence is 0.32 (see score help below)
1 MARKKLKKFTTLEIVLSVLLLVLFIISIVLIVLLAKESLKSTAPDPGTTG 50
51 TPDPGTTGTPDPGTTGTTHARTTGPPDPGTTGTTPVSAECPVVNELERIN 100
101 CIPDQPPTKATCDQRGCCWNPQGAVSVPWCYYSKNHSYHVEGNLVNTNAG 150
151 FTARLKNLPSSPVFGSNVDNVLLTAEYQTSNRFHFKLTDQTNNRFEVPHE 200
201 HVQSFSGNAAASLTYQVEISRQPFSIKVTRRSNNRVLFDSSIGPLLFADQ 250
251 FLQLSTRLPSTNVYGLGEHVHQQYRHDMNWKTWPIFNRDTTPNGNGTNLY 300
301 GAQTFFLCLEDASGLSFGVFLMNSNAMEVVLQPAPAITYRTIGGILDFYV 350
351 FLGNTPEQVVQEYLELIGRPALPSYWALGFHLSRYEYGTLDNMREVVERN 400
401 RAAQLPYDVQHADIDYMDERRDFTYDSVDFKGFPEFVNELHNNGQKLVII 450
451 VDPAISNNSSSSKPYGPYDRGSDMKIWVNSSDGVTPLIGEVWPGQTVFPD 500
501 YTNPNCAVWWTKEFELFHNQVEFDGIWIDMNEVSNFVDGSVSGCSTNNLN 550
551 NPPFTPRILDGYLFCKTLCMDAVQHWGKQYDIHNLYGYSMAVATAEAAKT 600
601 VFPNKRSFILTRSTFAGSGKFAAHWLGDNTATWDDLRWSIPGVLEFNLFG 650
651 IPMVGPDICGFALDTPEELCRRWMQLGAFYPFSRNHNGQGYKDQDPASFG 700
701 ADSLLLNSSRHYLNIRYTLLPYLYTLFFRAHSRGDTVARPLLHEFYEDNS 750
751 TWDVHQQFLWGPGLLITPVLDEGAEKVMAYVPDAVWYDYETGSQVRWRKQ 800
801 KVEMELPGDKIGLHLRGGYIFPTQQPNTTTLASRKNPLGLIIALDENKEA 850
851 KGELFWDNGETKDTVANKVYLLCEFSVTQNRLEVNISQSTYKDPNNLAFN 900
901 EIKILGTEEPSNVTVKHNGVPSQTSPTVTYDSNLKVAIITDIDLLLGEAY 950
951 TVEWSIKIRDEEKIDCYPDENGASAENCTARGCIWEASNSSGVPFCYFVN 1000
1001 DLYSVSDVQYNSHGATADISLKSSVYANAFPSTPVNPLRLDVTYHKNEML 1050
1051 QFKIYDPNKNRYEVPVPLNIPSMPSSTPEGQLYDVLIKKNPFGIEIRRKS 1100
1101 TGTIIWDSQLLGFTFSDMFIRISTRLPSKYLYGFGETEHRSYRRDLEWHT 1150
1151 WGMFSRDQPPGYKKNSYGVHPYYMGLEEDGSAHGVLLLNSNAMDVTFQPL 1200
1201 PALTYRTTGGVLDFYVFLGPTPELVTQQYTELIGRPVMVPYWSLGFQLCR 1250
1251 YGYQNDSEIASLYDEMVAAQIPYDVQYSDIDYMERQLDFTLSPKFAGFPA 1300
1301 LINRMKADGMRVILILDPAISGNETQPYPAFTRGVEDDVFIKYPNDGDIV 1350
1351 WGKVWPDFPDVVVNGSLDWDSQVELYRAYVAFPDFFRNSTAKWWKREIEE 1400
1401 LYNNPQNPERSLKFDGMWIDMNEPSSFVNGAVSPGCRDASLNHPPYMPHL 1450
1451 ESRDRGLSSKTLCMESQQILPDGSLVQHYNVHNLYGWSQTRPTYEAVQEV 1500
1501 TGQRGVVITRSTFPSSGRWAGHWLGDNTAAWDQLKKSIIGMMEFSLFGIS 1550
1551 YTGADICGFFQDAEYEMCVRWMQLGAFYPFSRNHNTIGTRRQDPVSWDAA 1600
1601 FVNISRNVLQTRYTLLPYLYTLMQKAHTEGVTVVRPLLHEFVSDQVTWDI 1650
1651 DSQFLLGPAFLVSPVLERNARNVTAYFPRARWYDYYTGVDINARGEWKTL 1700
1701 PAPLDHINLHVRGGYILPWQEPALNTHLSRKNPLGLIIALDENKEAKGEL 1750
1751 FWDDGQTKDTVAKKVYLLCEFSVTQNHLEVTISQSTYKDPNNLAFNEIKI 1800
1801 LGMEEPSNVTVKHNGVPSQTSPTVTYDSNLKVAIITDINLFLGEAYTVEW 1850
1851 SIKIRDEEKIDCYPDENGDSAENCTARGCIWEASNSSGVPFCYFVNDLYS 1900
1901 VSDVQYNSHGATADISLKSSVHANAFPSTPVNPLRLDVTYHKNEMLQFKI 1950
1951 YDPNNNRYEVPVPLNIPSVPSSTPEGQLYDVLIKKNPFGIEIRRKSTGTI 2000
2001 IWDSQLLGFTFNDMFIRISTRLPSKYLYGFGETEHTSYRRDLEWHTWGMF 2050
2051 SRDQPPGYKKNSYGVHPYYMGLEEDGSAHGVLLLNSNAMDVTFQPLPALT 2100
2101 YRTTGGVLDFYVFLGPTPELVTQQYTELIGRPVMVPYWSLGFQLCRYGYQ 2150
2151 NDSEISSLYDEMVAAQIPYDVQYSDIDYMERQLDFTLSPKFAGFPALINR 2200
2201 MKADGMRVILILDPAISGNETQPYPAFTRGVEDDVFIKYPNDGDIVWGKV 2250
2251 WPDFPDVVVNGSLDWDSQVELYRAYVAFPDFFRNSTAKWWKREIEELYNN 2300
2301 PQNPERSLKFDGMWIDMNEPSSFVNGAVSPGCRDASLNHPPYMPYLESRD 2350
2351 RGLSSKTLCMESQQILPDGSPVQHYNVHNLYGWSQTRPTYEAVQEVTGQR 2400
2401 GVVITRSTFPSSGRWAGHWLGDNTAAWDQLKKSIIGMMEFSLFGISYTGA 2450
2451 DICGFFQDAEYEMCVRWMQLGAFYPFSRNHNTIGTRRQDPVSWDVAFVNI 2500
2501 SRTVLQTRYTLLPYLYTLMHKAHTEGVTVVRPLLHEFVSDQVTWDIDSQF 2550
2551 LLGPAFLVSPVLERNARNVTAYFPRARWYDYYTGVDINARGEWKTLPAPL 2600
2601 DHINLHVRGGYILPWQEPALNTHLSRQKFMGFKIALDDEGTAGGWLFWDD 2650
2651 GQSIDTYGKGLYYLASFSASQNTMQSHIIFNNYITGTNPLKLGYIEIWGV 2700
2701 GSVPVTSVSISVSGMVITPSFNNDPTTQVLSIDVTDRNISLHNFTSLTWI 2750
2751 STL 2753
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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