 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38737 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MSISSDEAKEKQLVEKAELRLAIADSPQKFETNLQTFLPPLLLKLASPHA 50
51 SVRTAVFSALKNLISRINTLPQVQLPVRALIVQAKEPNLAAQQDSTNVRL 100
101 YSLLLASKGIDRLSLQDRQQLLPLVVSNISCLTGTVAARMFHILLKLILE 150
151 WVAPQESSHEQEEFVQFLQLDNDGFSFLMRQFTRFFLLVPSKQVQVSQQP 200
201 LSRGYTCPGLSLTDVAFFTYDAGVTFNKEQLNKFKKAIFQFVCRGMAATQ 250
251 TIEQSPRMIELMEFLCVVSTDSTNLSDDAAQFMKRFPMPYENEEFITFLQ 300
301 TLYIGNTANGRPPVKAILQEKILSILNRSHFATTKAECISLICSIGLHSS 350
351 EYKLRSLTLSFIRHVAKLNYKNLNPASSSPSSTDFSTCIVSLIRNNLHAE 400
401 GWPKLQLGPQTPAFNTAILQRQLQYETLGDILKRDFELVSDLSYIEFLFE 450
451 SLKNDLPQFRSSIQESLLSLVGHLSILPQQSKLKLKNLLRKNLSIDEQQR 500
501 EDNNDAVNSIMALKFVSIKFTNAAFPFHDPEARLFNIWGTVRTNRFDIIE 550
551 ESFKGLQPFWFRVNNASINTSATVKTSDLLGSHLSETEFPPFREFLQVLI 600
601 DQLDSEAASITRKSLNNAVRFSKQCLISNAIYGKKTMVIQDEDWSVRIDK 650
651 ALELDDTVVSRVNEMVQGMNDDIFIRYLTLLSNEFTATNSKGEQIAIFPY 700
701 QDPIFGSVLLTLLNFVSNNVLRRLEILVPDLYHLVIMKFQSLSDNDLAVC 750
751 ATIIGIISTAIADSTHVKRITKIAQSQTMAETYVASYVVPRLYLKDQTNH 800
801 IESDSILNLLNILTTHLSHPGTNKDMILKLVCQVTKFGLLLQVSAQERKD 850
851 FLKKVMDTIQDKLINDVTAIQTWSYLSLYSTDLENSSLFQEKLLETNVSK 900
901 QNDFLFSVGESLSVVAGKWSSKYLIKQIDIPNFNVEIMQQKFPATNVTTI 950
951 LDEIFSGCDSTKPSLRKASCIWLLSYIQYLGHLPEVSSKCNDIHLRFMRF 1000
1001 LADRDEFIQDSAARGLSLVYEIGGSDLKESMVKGLLKSFTESTAGSASTS 1050
1051 ATGISGSVSEETELFEPGVLNTGDGSISTYKDILNLASEVGDPALVYKFM 1100
1101 SLAKSSALWSSRKGIAFGLGAIMSKSSLEELLLKDQQTAKKLIPKLYRYR 1150
1151 FDPFQAVSRSMTDIWNTLIPESSLTISLYFNDILDELLCGMANKEWRVRE 1200
1201 ASTSALLQLIQSQPQEKFSEKMLKIWTMAFRTMDDIKDSVREVGTKFTTV 1250
1251 LAKILARSIDVEKGVNPTKSKEILDNILPFLWGPHGLNSDAEEVRNFALT 1300
1301 TLIDLVKHSPGAIKPFTPKLIYDFITLFSSIEPQVINYLALNAANYNIDA 1350
1351 NVIDTQRKNGVTNSPLFQTIEKLINNSDDCMMEEIINVVIKASRKSVGLP 1400
1401 SKVASSLVIIILVKRYSIEMKPYSGKLLKVCLTMFEDRNESVNIAFAISM 1450
1451 GYLFKVSALDKCIKYSEKLITKYFEPTSTENNKKVVGTAIDSILNYAKSE 1500
1501 FDNVASVFMPLIFIACNDEDKDLETLYNKIWTEASSSGAGTVKLYLPEIL 1550
1551 NVLCVNIKSNDFSIRKTCAKSVIQLCGGINDSIPYPQIVKLFDISREALS 1600
1601 GRSWDGKEHIVAALVSLTEKFSQTVADNNDLQESINHVMYTEVSRKSMKY 1650
1651 VKKILPLYARYINVNPQEETITFLIEKAKEMIRLLGSESDDSEGSIKQTS 1700
1701 DESTIKRIKPNTEITQKSSKENIENEEYVINLLKVSVDICNNSKSRYPMN 1750
1751 LLEFIIDEIAYLFHNDRIIHTWRTQLAASEIGISIVGRFSTISSADFIQN 1800
1801 VGRLWDQTFPINCNKETIENVKLQMIKFGGLIIQKIPSLQNNIEENLRLL 1850
1851 NSIDSTSRIELELKNIGL 1868
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.) |
Go back to the NucPred Home Page.